Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Abdollahzadeh, S.; Allili, M. S.; Boulmerka, A.; Lapointe, J. -F.
A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments Article de journal
Dans: IEEE Open Journal of the Computer Society, vol. 7, p. 492–503, 2026, ISSN: 26441268 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)
@article{abdollahzadeh_vision-based_2026,
title = {A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments},
author = {S. Abdollahzadeh and M. S. Allili and A. Boulmerka and J. -F. Lapointe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105029942397&doi=10.1109%2FOJCS.2026.3663268&partnerID=40&md5=b11484e035458c84b1d3f6780b92c91c},
doi = {10.1109/OJCS.2026.3663268},
issn = {26441268 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Open Journal of the Computer Society},
volume = {7},
pages = {492–503},
abstract = {Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.},
keywords = {Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)},
pubstate = {published},
tppubtype = {article}
}
Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.
2.
Lapointe, J. -F.; Allili, M. S.; Hammouche, N.
Field Trials of an AI-AR-Based System for Remote Bridge Inspection by Drone Article d'actes
Dans: D., Harris; W.-C., Li; H., Krömker (Ed.): Lect. Notes Comput. Sci., p. 278–287, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303176823-1 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Résumé | Liens | BibTeX | Étiquettes: Advanced systems, Air navigation, Artificial intelligence, artificial intelligence (AI), augmented reality, augmented reality (AR), Bridge inspection, Concrete bridges, Drone, Drones, Field trial, HIgh speed networks, High-speed Networks, Network links, Performance, Remote guidance, Transportation infrastructures, UAV
@inproceedings{lapointe_field_2025,
title = {Field Trials of an AI-AR-Based System for Remote Bridge Inspection by Drone},
author = {J. -F. Lapointe and M. S. Allili and N. Hammouche},
editor = {Harris D. and Li W.-C. and Krömker H.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213387549&doi=10.1007%2f978-3-031-76824-8_20&partnerID=40&md5=565ae5dded9cfdf27632e79e702c7718},
doi = {10.1007/978-3-031-76824-8_20},
isbn = {03029743 (ISSN); 978-303176823-1 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15381 LNCS},
pages = {278–287},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Bridge inspections are important to ensure the safety of users of these critical transportation infrastructures and avoid tragedies that could be caused by the collapse of these infrastructures. This paper describes the results of field trials of an advanced system for remotely guided inspection of bridges by a drone, which relies on artificial intelligence and augmented reality to achieve it. Results indicate that a high speed network link is critical to achieve good performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Advanced systems, Air navigation, Artificial intelligence, artificial intelligence (AI), augmented reality, augmented reality (AR), Bridge inspection, Concrete bridges, Drone, Drones, Field trial, HIgh speed networks, High-speed Networks, Network links, Performance, Remote guidance, Transportation infrastructures, UAV},
pubstate = {published},
tppubtype = {inproceedings}
}
Bridge inspections are important to ensure the safety of users of these critical transportation infrastructures and avoid tragedies that could be caused by the collapse of these infrastructures. This paper describes the results of field trials of an advanced system for remotely guided inspection of bridges by a drone, which relies on artificial intelligence and augmented reality to achieve it. Results indicate that a high speed network link is critical to achieve good performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
3.
Valem, L. P.; Pedronette, D. C. G.; Allili, M. S.
Contrastive Loss Based on Contextual Similarity for Image Classification Article d'actes
Dans: G., Bebis; V., Patel; J., Gu; J., Panetta; Y., Gingold; K., Johnsen; M.S., Arefin; S., Dutta; A., Biswas (Ed.): Lect. Notes Comput. Sci., p. 58–69, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303177391-4 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Résumé | Liens | BibTeX | Étiquettes: Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure
@inproceedings{valem_contrastive_2025,
title = {Contrastive Loss Based on Contextual Similarity for Image Classification},
author = {L. P. Valem and D. C. G. Pedronette and M. S. Allili},
editor = {Bebis G. and Patel V. and Gu J. and Panetta J. and Gingold Y. and Johnsen K. and Arefin M.S. and Dutta S. and Biswas A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218461565&doi=10.1007%2f978-3-031-77392-1_5&partnerID=40&md5=cf885303646c3b1a4f4eacb87d02a2b6},
doi = {10.1007/978-3-031-77392-1_5},
isbn = {03029743 (ISSN); 978-303177391-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15046 LNCS},
pages = {58–69},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure},
pubstate = {published},
tppubtype = {inproceedings}
}
Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
4.
Allaoui, M.; Hedjam, R.; Bouanane, K.; Allili, M. S.; Kherfi, M. L.; Belhaouari, S. B.
Exploring non-negativity for improved manifold embedding: Application to t-SNE Article de journal
Dans: Knowledge-Based Systems, vol. 330, 2025, ISSN: 09507051 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Dimensionality reduction, Embedding technique, Embeddings, Gradient methods, Gradient-descent, Manifold embedding, Matrix algebra, Non-negative matrix factorization, Non-negativity, Nonnegative matrix factorization, Nonnegativity constraints, Performance, T-SNE
@article{allaoui_exploring_2025,
title = {Exploring non-negativity for improved manifold embedding: Application to t-SNE},
author = {M. Allaoui and R. Hedjam and K. Bouanane and M. S. Allili and M. L. Kherfi and S. B. Belhaouari},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105018098090&doi=10.1016%2Fj.knosys.2025.114547&partnerID=40&md5=237540c38a928146d589b96cd6888547},
doi = {10.1016/j.knosys.2025.114547},
issn = {09507051 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Knowledge-Based Systems},
volume = {330},
abstract = {Drawing inspiration from Non-negative Matrix Factorization (NMF), this paper explores the potential of incorporating non-negativity constraints into embedding techniques, with a focus on t-SNE as an application. Specifically, we investigate the following questions: Can enforcing non-negativity in the embedding space enhance interpretability and improve the quality of embedded data? By prioritizing non-negativity, can embedding methods achieve better performance and more meaningful representations? Additionally, does enforcing non-negativity in the embedded space help preserve both the local and global structure of data in the manifold, leading to more accurate and interpretable embeddings? In this work, we could show both objectively and subjectively how enforcing t-SNE to leverage the non-negativity of the data addresses the raised questions. To achieve this, we introduced a novel approach to transforming the additive update rule of the gradient descent used by t-SNE to a multiplicative counterpart to enforce the non-negativity in the embedded space. However, grappling with full non-negativity in the gradient descent formula presents challenges, prompting our focus solely on the (yi−yj) term, resulting in a semi-non-negative t-SNE algorithm, shortly named SN-tSNE. Nevertheless, experimental findings substantiate the significant impact of the proposed update rule on the performance and efficacy of the SN-tSNE algorithm. Furthermore, additional experiments are performed to compare SN-tSNE with its precursor t-SNE, as well as the competitive embedding technique UMAP, alongside other relevant embedding and dimensionality reduction models like NMF. The source code of SN-tSNE is available on GitHub (https://github.com/M-Allaoui/SN-tSNE.git). © 2025},
keywords = {Dimensionality reduction, Embedding technique, Embeddings, Gradient methods, Gradient-descent, Manifold embedding, Matrix algebra, Non-negative matrix factorization, Non-negativity, Nonnegative matrix factorization, Nonnegativity constraints, Performance, T-SNE},
pubstate = {published},
tppubtype = {article}
}
Drawing inspiration from Non-negative Matrix Factorization (NMF), this paper explores the potential of incorporating non-negativity constraints into embedding techniques, with a focus on t-SNE as an application. Specifically, we investigate the following questions: Can enforcing non-negativity in the embedding space enhance interpretability and improve the quality of embedded data? By prioritizing non-negativity, can embedding methods achieve better performance and more meaningful representations? Additionally, does enforcing non-negativity in the embedded space help preserve both the local and global structure of data in the manifold, leading to more accurate and interpretable embeddings? In this work, we could show both objectively and subjectively how enforcing t-SNE to leverage the non-negativity of the data addresses the raised questions. To achieve this, we introduced a novel approach to transforming the additive update rule of the gradient descent used by t-SNE to a multiplicative counterpart to enforce the non-negativity in the embedded space. However, grappling with full non-negativity in the gradient descent formula presents challenges, prompting our focus solely on the (yi−yj) term, resulting in a semi-non-negative t-SNE algorithm, shortly named SN-tSNE. Nevertheless, experimental findings substantiate the significant impact of the proposed update rule on the performance and efficacy of the SN-tSNE algorithm. Furthermore, additional experiments are performed to compare SN-tSNE with its precursor t-SNE, as well as the competitive embedding technique UMAP, alongside other relevant embedding and dimensionality reduction models like NMF. The source code of SN-tSNE is available on GitHub (https://github.com/M-Allaoui/SN-tSNE.git). © 2025
5.
Bacha, S.; Allili, M. S.; Kerbedj, T.; Chahboub, R.
Investigating food pairing hypothesis based on deep learning: Case of Algerian cuisine Article de journal
Dans: International Journal of Gastronomy and Food Science, vol. 39, 2025, ISSN: 1878450X (ISSN), (Publisher: AZTI-Tecnalia).
Résumé | Liens | BibTeX | Étiquettes: Algerian cuisine, Computational gastronomy, Deep learning, Food pairing hypothesis (FPH), Spectral clustering
@article{bacha_investigating_2025,
title = {Investigating food pairing hypothesis based on deep learning: Case of Algerian cuisine},
author = {S. Bacha and M. S. Allili and T. Kerbedj and R. Chahboub},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214793354&doi=10.1016%2Fj.ijgfs.2024.101098&partnerID=40&md5=8e97f1f57acc30a9d2d2f02593ecc69b},
doi = {10.1016/j.ijgfs.2024.101098},
issn = {1878450X (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {International Journal of Gastronomy and Food Science},
volume = {39},
abstract = {Traditional cuisine is considered a core element of cultural identity. The choice of food can often be influenced by identity, culture, and geography. This work investigates the traditional Algerian cuisine by exploring the food pairing hypothesis, which stipulates that combined ingredients with common flavor compounds taste better than their counterpart. To gain insight into the ingredients compounds found in this cuisine, we analyze their characteristics using spectral clustering. Then, we propose a model based on LSTMs to test the food pairing hypothesis in the Algerian cuisine on a collected corpus. Our research shows that the Algerian cuisine has a negative food pairing tendency, which is consistent with the South European cuisine, suggesting broader regional culinary patterns. To the best of our knowledge, this is the first study to investigate the FPH in Algerian cuisine, contributing to a deeper understanding of the food pairing tendencies specific to this region and offering a comparative perspective with neighboring Mediterranean cuisines. © 2025 Elsevier B.V.},
note = {Publisher: AZTI-Tecnalia},
keywords = {Algerian cuisine, Computational gastronomy, Deep learning, Food pairing hypothesis (FPH), Spectral clustering},
pubstate = {published},
tppubtype = {article}
}
Traditional cuisine is considered a core element of cultural identity. The choice of food can often be influenced by identity, culture, and geography. This work investigates the traditional Algerian cuisine by exploring the food pairing hypothesis, which stipulates that combined ingredients with common flavor compounds taste better than their counterpart. To gain insight into the ingredients compounds found in this cuisine, we analyze their characteristics using spectral clustering. Then, we propose a model based on LSTMs to test the food pairing hypothesis in the Algerian cuisine on a collected corpus. Our research shows that the Algerian cuisine has a negative food pairing tendency, which is consistent with the South European cuisine, suggesting broader regional culinary patterns. To the best of our knowledge, this is the first study to investigate the FPH in Algerian cuisine, contributing to a deeper understanding of the food pairing tendencies specific to this region and offering a comparative perspective with neighboring Mediterranean cuisines. © 2025 Elsevier B.V.
6.
Zetout, A.; Allili, M. S.
CSDNet: Context-Aware Segmentation of Disaster Aerial Imagery Using Detection-Guided Features and Lightweight Transformers Article de journal
Dans: Remote Sensing, vol. 17, no 14, 2025, ISSN: 20724292 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Aerial imagery, Affected area, Antennas, Class imbalance, Context-Aware, Contextual semantic segmentation, Contextual semantics, Detection, disaster response, Disaster-response, Emergency services, Error detection, Feature extraction, Lightweight model, Semantic segmentation, Semantics
@article{zetout_csdnet_2025,
title = {CSDNet: Context-Aware Segmentation of Disaster Aerial Imagery Using Detection-Guided Features and Lightweight Transformers},
author = {A. Zetout and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011677142&doi=10.3390%2Frs17142337&partnerID=40&md5=a83db334b208d065476e0026ad0ee416},
doi = {10.3390/rs17142337},
issn = {20724292 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Remote Sensing},
volume = {17},
number = {14},
abstract = {Accurate multi-class semantic segmentation of disaster-affected areas is essential for rapid response and effective recovery planning. We present CSDNet, a context-aware segmentation model tailored to disaster scene scenarios, designed to improve segmentation of both large-scale disaster zones and small, underrepresented classes. The architecture combines a lightweight transformer module for global context modeling with depthwise separable convolutions (DWSCs) to enhance efficiency without compromising representational capacity. Additionally, we introduce a detection-guided feature fusion mechanism that integrates outputs from auxiliary detection tasks to mitigate class imbalance and improve discrimination of visually similar categories. Extensive experiments on several public datasets demonstrate that our model significantly improves segmentation of both man-made infrastructure and natural damage-related features, offering a robust and efficient solution for post-disaster analysis. © 2025 by the authors.},
keywords = {Aerial imagery, Affected area, Antennas, Class imbalance, Context-Aware, Contextual semantic segmentation, Contextual semantics, Detection, disaster response, Disaster-response, Emergency services, Error detection, Feature extraction, Lightweight model, Semantic segmentation, Semantics},
pubstate = {published},
tppubtype = {article}
}
Accurate multi-class semantic segmentation of disaster-affected areas is essential for rapid response and effective recovery planning. We present CSDNet, a context-aware segmentation model tailored to disaster scene scenarios, designed to improve segmentation of both large-scale disaster zones and small, underrepresented classes. The architecture combines a lightweight transformer module for global context modeling with depthwise separable convolutions (DWSCs) to enhance efficiency without compromising representational capacity. Additionally, we introduce a detection-guided feature fusion mechanism that integrates outputs from auxiliary detection tasks to mitigate class imbalance and improve discrimination of visually similar categories. Extensive experiments on several public datasets demonstrate that our model significantly improves segmentation of both man-made infrastructure and natural damage-related features, offering a robust and efficient solution for post-disaster analysis. © 2025 by the authors.
7.
Abdollahzadeh, S.; Allili, M. S.; Boulmerka, A.; Lapointe, J. -F.
Visual Safety Mapping for UAV Landings Using Ordinal Regression Networks Article de journal
Dans: IEEE Transactions on Artificial Intelligence, 2025, ISSN: 26914581 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: automatic UAV navigation, deep ordinal regression, safe landing zones (SLZ), Semantic segmentation
@article{abdollahzadeh_visual_2025,
title = {Visual Safety Mapping for UAV Landings Using Ordinal Regression Networks},
author = {S. Abdollahzadeh and M. S. Allili and A. Boulmerka and J. -F. Lapointe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105023324811&doi=10.1109%2FTAI.2025.3635093&partnerID=40&md5=14d5d4e4558cf5f4db08bd7d2a61a945},
doi = {10.1109/TAI.2025.3635093},
issn = {26914581 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Artificial Intelligence},
abstract = {As Unmanned Aerial Vehicles (UAVs) see growing use in civilian applications, reliably identifying Safe Landing Zones (SLZs) in varied environments is essential for autonomous navigation and emergency response. Passive vision sensors offer a low-cost, lightweight solution for real-time terrain analysis and 3D scene reconstruction, making them ideal for onboard systems. We introduce OR-SLZNet, an original deep learning model based on ordinal regression to predict SLZs from UAV imagery. Unlike prior approaches, OR-SLZNet produces dense, multi-level safety maps by jointly leveraging photometric (e.g., color and texture) and geometric cues (e.g., flatness, slope, and depth), assigning each pixel an ordinal safety score that reflects landing suitability. With real-time inference (textasciitilde0.02s/frame), the model supports onboard deployment and rapid decision-making in time-critical situations. Extensive experiments on five diverse datasets demonstrate OR-SLZNet effectiveness and strong generalization across a wide range of structural complexities. © 2020 IEEE.},
keywords = {automatic UAV navigation, deep ordinal regression, safe landing zones (SLZ), Semantic segmentation},
pubstate = {published},
tppubtype = {article}
}
As Unmanned Aerial Vehicles (UAVs) see growing use in civilian applications, reliably identifying Safe Landing Zones (SLZs) in varied environments is essential for autonomous navigation and emergency response. Passive vision sensors offer a low-cost, lightweight solution for real-time terrain analysis and 3D scene reconstruction, making them ideal for onboard systems. We introduce OR-SLZNet, an original deep learning model based on ordinal regression to predict SLZs from UAV imagery. Unlike prior approaches, OR-SLZNet produces dense, multi-level safety maps by jointly leveraging photometric (e.g., color and texture) and geometric cues (e.g., flatness, slope, and depth), assigning each pixel an ordinal safety score that reflects landing suitability. With real-time inference (textasciitilde0.02s/frame), the model supports onboard deployment and rapid decision-making in time-critical situations. Extensive experiments on five diverse datasets demonstrate OR-SLZNet effectiveness and strong generalization across a wide range of structural complexities. © 2020 IEEE.
8.
Allaoui, M. L.; Allili, M. S.; Belaid, A.
HA-U3Net: A modality-agnostic framework for 3D medical image segmentation using nested V-Net structure and hybrid attention Article de journal
Dans: Knowledge-Based Systems, vol. 327, 2025, ISSN: 09507051 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: 3D medical image, 3D medical image segmentation, Diagnosis, Diagnosis planning, Disease diagnosis, Disease treatment, Generalization capability, Image segmentation, Magnetic resonance imaging, Medical image processing, Medical image segmentation, Nested volume-structure, Net structures, Self hybrid attention, Structures (built objects)
@article{allaoui_ha-u3net_2025,
title = {HA-U3Net: A modality-agnostic framework for 3D medical image segmentation using nested V-Net structure and hybrid attention},
author = {M. L. Allaoui and M. S. Allili and A. Belaid},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011370963&doi=10.1016%2Fj.knosys.2025.114127&partnerID=40&md5=d98a109f015445adb3001bb4017bf953},
doi = {10.1016/j.knosys.2025.114127},
issn = {09507051 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Knowledge-Based Systems},
volume = {327},
abstract = {3D medical image segmentation is essential for disease diagnosis and treatment planning across a wide range of imaging modalities (e.g., MRI, CT, ultrasound, and PET). However, modality-specific challenges, such as noise, artifacts, low contrast, and anatomical variability, along with the presence of small lesions and fuzzy boundaries, hinder the generalization capability of existing segmentation models. In this work, we present HA-U3Net, a novel 3D U-Net-based model designed to address these limitations through a stepwise approach. First, we introduce a deeply nested U3-shaped structure built upon 3D V-Net modules, enabling multi-scale hierarchical representation learning. Second, we integrate a hybrid attention mechanism combining spatial and channel-wise attention to enhance salient features extraction and the delineation of small or poorly defined structures. Third, we demonstrate the cross-modality generalization capabilities of HA-U3Net through extensive evaluations on several datasets, where our model consistently outperforms baseline methods. Finally, we propose a lightweight variant, U3Mamba, reducing computational complexity while maintaining high performance. © 2025 Elsevier B.V.},
keywords = {3D medical image, 3D medical image segmentation, Diagnosis, Diagnosis planning, Disease diagnosis, Disease treatment, Generalization capability, Image segmentation, Magnetic resonance imaging, Medical image processing, Medical image segmentation, Nested volume-structure, Net structures, Self hybrid attention, Structures (built objects)},
pubstate = {published},
tppubtype = {article}
}
3D medical image segmentation is essential for disease diagnosis and treatment planning across a wide range of imaging modalities (e.g., MRI, CT, ultrasound, and PET). However, modality-specific challenges, such as noise, artifacts, low contrast, and anatomical variability, along with the presence of small lesions and fuzzy boundaries, hinder the generalization capability of existing segmentation models. In this work, we present HA-U3Net, a novel 3D U-Net-based model designed to address these limitations through a stepwise approach. First, we introduce a deeply nested U3-shaped structure built upon 3D V-Net modules, enabling multi-scale hierarchical representation learning. Second, we integrate a hybrid attention mechanism combining spatial and channel-wise attention to enhance salient features extraction and the delineation of small or poorly defined structures. Third, we demonstrate the cross-modality generalization capabilities of HA-U3Net through extensive evaluations on several datasets, where our model consistently outperforms baseline methods. Finally, we propose a lightweight variant, U3Mamba, reducing computational complexity while maintaining high performance. © 2025 Elsevier B.V.
9.
Amirkhani, D.; Allili, M. S.; Lapointe, J. -F.
CrackSight: An Efficient Crack Segmentation Model in Varying Acquisition Ranges and Complex Backgrounds Article de journal
Dans: IEEE Transactions on Automation Science and Engineering, vol. 22, p. 19197–19214, 2025, ISSN: 15455955 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Attention mechanisms, Codes (symbols), Complex background, complex backgrounds, Crack detection, Crack propagation, Crack segmentation, Crack segmentations, Detection features, End to end, Feature extraction, Features extraction, Global context, Image segmentation, Learning models, Learning systems, Segmentation models, Transportation infrastructures
@article{amirkhani_cracksight_2025,
title = {CrackSight: An Efficient Crack Segmentation Model in Varying Acquisition Ranges and Complex Backgrounds},
author = {D. Amirkhani and M. S. Allili and J. -F. Lapointe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011756992&doi=10.1109%2FTASE.2025.3591407&partnerID=40&md5=d908b79e863a4725d10bec325b761f34},
doi = {10.1109/TASE.2025.3591407},
issn = {15455955 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Automation Science and Engineering},
volume = {22},
pages = {19197–19214},
abstract = {Accurate crack segmentation in concrete transportation infrastructures is critical for ensuring structural integrity and facilitating timely maintenance interventions. This paper presents CrackSight, an end-to-end deep learning model for precise crack segmentation across varying observational ranges and extremely complex backgrounds. CrackSight seamlessly integrates crack detection and segmentation through two branches. The Detection Feature Extraction Branch (DFEB) provides global context for crack localization in complex backgrounds or at far observation ranges. It guides the segmentation model to focus on regions with the highest crack-prone potential. The segmentation branch leverages the fusion of multi-scale feature maps using dilated convolutions, allowing to capture subtle and complex crack patterns. The branch also incorporates the Dual-Attention Linear Focus Mechanism (DALFM) enhancing crack segmentation through saliency-driven improvements. Finally, CrackSight uses a novel hybrid contextual loss, which dynamically compensates for class imbalance and enhance crack discrimination against complex backgrounds. Our model is also lightweight and can be run in resource-constrained environments, making it suitable for real-world inspection using mobile platforms. Our results demonstrate that it significantly improves segmentation accuracy, setting a new benchmark for crack segmentation. The dataset and additional resources are available on GitHub. Note to Practitioners—CrackSight is a dual-branch deep learning framework designed for accurate and efficient segmentation of concrete cracks under challenging real-world conditions. By combining a detection-guided localization branch with a context-aware segmentation, CrackSight offers enhanced robustness to noise, background clutter, and varying acquisition distances, common challenges in UAV-based infrastructure inspections. Its architecture integrates multi-scale feature fusion and adaptive contextual guidance, enabling reliable detection of both fine and fragmented cracks. With its lightweight design and fast inference time, CrackSight offers practitioners a practical and scalable solution for automating visual inspection tasks, reducing manual effort, and improving safety in structural health monitoring workflows. © 2025 IEEE.},
keywords = {Attention mechanisms, Codes (symbols), Complex background, complex backgrounds, Crack detection, Crack propagation, Crack segmentation, Crack segmentations, Detection features, End to end, Feature extraction, Features extraction, Global context, Image segmentation, Learning models, Learning systems, Segmentation models, Transportation infrastructures},
pubstate = {published},
tppubtype = {article}
}
Accurate crack segmentation in concrete transportation infrastructures is critical for ensuring structural integrity and facilitating timely maintenance interventions. This paper presents CrackSight, an end-to-end deep learning model for precise crack segmentation across varying observational ranges and extremely complex backgrounds. CrackSight seamlessly integrates crack detection and segmentation through two branches. The Detection Feature Extraction Branch (DFEB) provides global context for crack localization in complex backgrounds or at far observation ranges. It guides the segmentation model to focus on regions with the highest crack-prone potential. The segmentation branch leverages the fusion of multi-scale feature maps using dilated convolutions, allowing to capture subtle and complex crack patterns. The branch also incorporates the Dual-Attention Linear Focus Mechanism (DALFM) enhancing crack segmentation through saliency-driven improvements. Finally, CrackSight uses a novel hybrid contextual loss, which dynamically compensates for class imbalance and enhance crack discrimination against complex backgrounds. Our model is also lightweight and can be run in resource-constrained environments, making it suitable for real-world inspection using mobile platforms. Our results demonstrate that it significantly improves segmentation accuracy, setting a new benchmark for crack segmentation. The dataset and additional resources are available on GitHub. Note to Practitioners—CrackSight is a dual-branch deep learning framework designed for accurate and efficient segmentation of concrete cracks under challenging real-world conditions. By combining a detection-guided localization branch with a context-aware segmentation, CrackSight offers enhanced robustness to noise, background clutter, and varying acquisition distances, common challenges in UAV-based infrastructure inspections. Its architecture integrates multi-scale feature fusion and adaptive contextual guidance, enabling reliable detection of both fine and fragmented cracks. With its lightweight design and fast inference time, CrackSight offers practitioners a practical and scalable solution for automating visual inspection tasks, reducing manual effort, and improving safety in structural health monitoring workflows. © 2025 IEEE.
10.
Allaoui, M. L.; Allili, M. S.
MixLVMM: A Mixture of Lightweight Vision Mamba Model for Enhancing Skin Lesion Segmentation Across High Tone Variability Article de journal
Dans: IEEE Access, vol. 13, p. 121234–121249, 2025, ISSN: 21693536 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Attention mechanism, Attention mechanisms, Computational efficiency, Critical challenges, Dermatology, Diagnosis, Image segmentation, Lesion segmentations, Lung cancer, Mixture of experts model, Mixture-of-experts model, Segmentation performance, Skin lesion, Skin lesion segmentation, Skin/lesion tone variability, Vision mamba
@article{allaoui_mixlvmm_2025,
title = {MixLVMM: A Mixture of Lightweight Vision Mamba Model for Enhancing Skin Lesion Segmentation Across High Tone Variability},
author = {M. L. Allaoui and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105012036322&doi=10.1109%2FACCESS.2025.3588476&partnerID=40&md5=1cf51dcf43653e1677ad36a1360392ac},
doi = {10.1109/ACCESS.2025.3588476},
issn = {21693536 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Access},
volume = {13},
pages = {121234–121249},
abstract = {Accurate skin lesion segmentation remains a critical challenge in automated dermatological diagnosis due to heterogeneous lesion presentations, ambiguous boundaries, imaging artifacts, and significant variability in skin and lesion tones across diverse populations. Current segmentation methods inadequately address these multifaceted complexities, particularly failing to handle extreme tone variations that can lead to diagnostic bias. To address these limitations, we present the Mixture of Lightweight Vision Mamba Model (MixLVMM), a novel expert-based framework that enhances segmentation performance across high tone variability through specialized processing. Our approach employs a Siamese network with triplet loss as a gate mechanism to categorize lesions based on tonal characteristics, routing each image to specialized Vision Mamba Model (VMM) experts optimized for specific lesion categories. Each expert utilizes a U-shaped architecture incorporating Focused Vision Mamba blocks and Adaptive Salient Region Attention modules to capture lesion-specific features while maintaining computational efficiency. Comprehensive evaluation on ISIC and PH2 datasets demonstrates that MixLVMM achieves superior segmentation performance with an average Dice coefficient of 93%, surpassing state-of-the-art methods while maintaining efficiency with only 2.5M parameters. These results establish MixLVMM as a robust solution for addressing tone-related segmentation challenges in clinical dermatology, offering both high accuracy and practical deployment feasibility for real-world applications. © 2013 IEEE.},
keywords = {Attention mechanism, Attention mechanisms, Computational efficiency, Critical challenges, Dermatology, Diagnosis, Image segmentation, Lesion segmentations, Lung cancer, Mixture of experts model, Mixture-of-experts model, Segmentation performance, Skin lesion, Skin lesion segmentation, Skin/lesion tone variability, Vision mamba},
pubstate = {published},
tppubtype = {article}
}
Accurate skin lesion segmentation remains a critical challenge in automated dermatological diagnosis due to heterogeneous lesion presentations, ambiguous boundaries, imaging artifacts, and significant variability in skin and lesion tones across diverse populations. Current segmentation methods inadequately address these multifaceted complexities, particularly failing to handle extreme tone variations that can lead to diagnostic bias. To address these limitations, we present the Mixture of Lightweight Vision Mamba Model (MixLVMM), a novel expert-based framework that enhances segmentation performance across high tone variability through specialized processing. Our approach employs a Siamese network with triplet loss as a gate mechanism to categorize lesions based on tonal characteristics, routing each image to specialized Vision Mamba Model (VMM) experts optimized for specific lesion categories. Each expert utilizes a U-shaped architecture incorporating Focused Vision Mamba blocks and Adaptive Salient Region Attention modules to capture lesion-specific features while maintaining computational efficiency. Comprehensive evaluation on ISIC and PH2 datasets demonstrates that MixLVMM achieves superior segmentation performance with an average Dice coefficient of 93%, surpassing state-of-the-art methods while maintaining efficiency with only 2.5M parameters. These results establish MixLVMM as a robust solution for addressing tone-related segmentation challenges in clinical dermatology, offering both high accuracy and practical deployment feasibility for real-world applications. © 2013 IEEE.