Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Shangwe, C. N.; Davoust, A.; Khoury, R.
Beyond Detection: Evaluating LLMs’ Semantic Understanding of Code Vulnerabilities Proceedings Article
In: K., Adi; O., Nguena Timo; N., Boulahia-Cuppens; D., Espes; N., Stakhanova; M., Omar (Ed.): Lect. Notes Comput. Sci., pp. 19–34, Springer Science and Business Media Deutschland GmbH, 2026, ISBN: 03029743 (ISSN); 978-303220731-9 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Abstract | Links | BibTeX | Tags: Analysis workflow, Code Semantics, Codes (symbols), Critical questions, Development workflow, Interpretability, Language model, Large language model, Large Language Models (LLMs), Model semantics, Pattern matching, Semantics, Semantics understanding, Software design, Vulnerability detection
@inproceedings{shangweDetectionEvaluatingLLMs2026,
title = {Beyond Detection: Evaluating LLMs’ Semantic Understanding of Code Vulnerabilities},
author = {C. N. Shangwe and A. Davoust and R. Khoury},
editor = {Adi K. and Nguena Timo O. and Boulahia-Cuppens N. and Espes D. and Stakhanova N. and Omar M.},
url = {https://www.scopus.com/pages/publications/105046105995?origin=resultslist},
doi = {10.1007/978-3-032-20732-6_2},
isbn = {03029743 (ISSN); 978-303220731-9 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {16295 LNCS},
pages = {19–34},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {As Large Language Models (LLMs) become increasingly integrated into software development and analysis workflows, a critical question arises: do these models truly understand the semantics of code, or do they merely excel at pattern matching? Our goal is to assess the extent to which LLMs can back their predictions in vulnerability detection by correctly attributing the identified vulnerabilities to the violation of particular rules as proof that their decision is based on actual code semantics understanding. We employed the SVEN dataset, composed of function-level code snippets, to conduct a series of experiments that evaluate both the model’s ability to detect vulnerabilities and attribute predictions to the correct violated rule and measure LLMs’ performance under varying experimental setups. Our findings reveal that while LLMs achieve reasonable accuracy in vulnerability detection, a significant drop in performance is observed when correct rule attribution is also required, exposing a gap between perceived accuracy and actual accuracy. The difference between actual and perceived accuracy offers critical insight into the depth of code semantics understanding of LLMs in vulnerability detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Analysis workflow, Code Semantics, Codes (symbols), Critical questions, Development workflow, Interpretability, Language model, Large language model, Large Language Models (LLMs), Model semantics, Pattern matching, Semantics, Semantics understanding, Software design, Vulnerability detection},
pubstate = {published},
tppubtype = {inproceedings}
}
As Large Language Models (LLMs) become increasingly integrated into software development and analysis workflows, a critical question arises: do these models truly understand the semantics of code, or do they merely excel at pattern matching? Our goal is to assess the extent to which LLMs can back their predictions in vulnerability detection by correctly attributing the identified vulnerabilities to the violation of particular rules as proof that their decision is based on actual code semantics understanding. We employed the SVEN dataset, composed of function-level code snippets, to conduct a series of experiments that evaluate both the model’s ability to detect vulnerabilities and attribute predictions to the correct violated rule and measure LLMs’ performance under varying experimental setups. Our findings reveal that while LLMs achieve reasonable accuracy in vulnerability detection, a significant drop in performance is observed when correct rule attribution is also required, exposing a gap between perceived accuracy and actual accuracy. The difference between actual and perceived accuracy offers critical insight into the depth of code semantics understanding of LLMs in vulnerability detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
2.
Amirkhani, D.; Allili, M. S.; Lapointe, J. -F.
CrackSight: An Efficient Crack Segmentation Model in Varying Acquisition Ranges and Complex Backgrounds Journal Article
In: IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 19197–19214, 2025, ISSN: 15455955 (ISSN).
Abstract | Links | BibTeX | Tags: 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.
3.
Amirkhani, D.; Allili, M. S.; Lapointe, J. -F.
CrackSight: An Efficient Crack Segmentation Model in Varying Acquisition Ranges and Complex Backgrounds Journal Article
In: IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 19197–19214, 2025, ISSN: 15455955 (ISSN).
Abstract | Links | BibTeX | Tags: 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{amirkhaniCrackSightEfficientCrack2025,
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/pages/publications/105011756992?origin=resultslist},
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},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
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.
4.
Amirkhani, D.; Allili, M. S.; Lapointe, J. -F.
CrackSight: An Efficient Crack Segmentation Model in Varying Acquisition Ranges and Complex Backgrounds Journal Article
In: IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 19197–19214, 2025, ISSN: 15455955 (ISSN).
Abstract | Links | BibTeX | Tags: 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{amirkhaniCrackSightEfficientCrack2025a,
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.
5.
Amirkhani, D.; Allili, M. S.; Lapointe, J. -F.
CrackSight: An Efficient Crack Segmentation Model in Varying Acquisition Ranges and Complex Backgrounds Journal Article
In: IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 19197–19214, 2025, ISSN: 15455955 (ISSN).
Abstract | Links | BibTeX | Tags: 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{amirkhaniCrackSightEfficientCrack2025b,
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.