Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Hebbache, L.; Amirkhani, D.; Allili, M. S.; Hammouche, N.; Lapointe, J. -F.
Leveraging Saliency in Single-Stage Multi-Label Concrete Defect Detection Using Unmanned Aerial Vehicle Imagery Article de journal
Dans: Remote Sensing, vol. 15, no 5, 2023, ISSN: 20724292, (Publisher: MDPI).
Résumé | Liens | BibTeX | Étiquettes: Aerial vehicle, Aircraft detection, Antennas, Computational efficiency, Concrete defects, Deep learning, Defect detection, extraction, Feature extraction, Features extraction, Image acquisition, Image Enhancement, Multi-labels, One-stage concrete defect detection, Saliency, Single stage, Unmanned aerial vehicles (UAV), Unmanned areal vehicle imagery
@article{hebbache_leveraging_2023,
title = {Leveraging Saliency in Single-Stage Multi-Label Concrete Defect Detection Using Unmanned Aerial Vehicle Imagery},
author = {L. Hebbache and D. Amirkhani and M. S. Allili and N. Hammouche and J. -F. Lapointe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149966766&doi=10.3390%2frs15051218&partnerID=40&md5=7bf1cb3353270c696c07ff24dc24655d},
doi = {10.3390/rs15051218},
issn = {20724292},
year = {2023},
date = {2023-01-01},
journal = {Remote Sensing},
volume = {15},
number = {5},
abstract = {Visual inspection of concrete structures using Unmanned Areal Vehicle (UAV) imagery is a challenging task due to the variability of defects’ size and appearance. This paper proposes a high-performance model for automatic and fast detection of bridge concrete defects using UAV-acquired images. Our method, coined the Saliency-based Multi-label Defect Detector (SMDD-Net), combines pyramidal feature extraction and attention through a one-stage concrete defect detection model. The attention module extracts local and global saliency features, which are scaled and integrated with the pyramidal feature extraction module of the network using the max-pooling, multiplication, and residual skip connections operations. This has the effect of enhancing the localisation of small and low-contrast defects, as well as the overall accuracy of detection in varying image acquisition ranges. Finally, a multi-label loss function detection is used to identify and localise overlapping defects. The experimental results on a standard dataset and real-world images demonstrated the performance of SMDD-Net with regard to state-of-the-art techniques. The accuracy and computational efficiency of SMDD-Net make it a suitable method for UAV-based bridge structure inspection. © 2023 by the authors.},
note = {Publisher: MDPI},
keywords = {Aerial vehicle, Aircraft detection, Antennas, Computational efficiency, Concrete defects, Deep learning, Defect detection, extraction, Feature extraction, Features extraction, Image acquisition, Image Enhancement, Multi-labels, One-stage concrete defect detection, Saliency, Single stage, Unmanned aerial vehicles (UAV), Unmanned areal vehicle imagery},
pubstate = {published},
tppubtype = {article}
}
Visual inspection of concrete structures using Unmanned Areal Vehicle (UAV) imagery is a challenging task due to the variability of defects’ size and appearance. This paper proposes a high-performance model for automatic and fast detection of bridge concrete defects using UAV-acquired images. Our method, coined the Saliency-based Multi-label Defect Detector (SMDD-Net), combines pyramidal feature extraction and attention through a one-stage concrete defect detection model. The attention module extracts local and global saliency features, which are scaled and integrated with the pyramidal feature extraction module of the network using the max-pooling, multiplication, and residual skip connections operations. This has the effect of enhancing the localisation of small and low-contrast defects, as well as the overall accuracy of detection in varying image acquisition ranges. Finally, a multi-label loss function detection is used to identify and localise overlapping defects. The experimental results on a standard dataset and real-world images demonstrated the performance of SMDD-Net with regard to state-of-the-art techniques. The accuracy and computational efficiency of SMDD-Net make it a suitable method for UAV-based bridge structure inspection. © 2023 by the authors.
2.
Allili, M. S.; Ziou, D.
Object of interest segmentation and tracking by using feature selection and active contours Article d'actes
Dans: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, 2007, ISBN: 1-4244-1180-7 978-1-4244-1180-1, (ISSN: 10636919).
Résumé | Liens | BibTeX | Étiquettes: Feature extraction, Image acquisition, Image segmentation, Object recognition, Object segmentation, Objective functions, Optimization
@inproceedings{allili_object_2007,
title = {Object of interest segmentation and tracking by using feature selection and active contours},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34948855864&doi=10.1109%2fCVPR.2007.383449&partnerID=40&md5=2429a266190c72bb8fb8d3776c444906},
doi = {10.1109/CVPR.2007.383449},
isbn = {1-4244-1180-7 978-1-4244-1180-1},
year = {2007},
date = {2007-01-01},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
address = {Minneapolis, MN},
abstract = {Most image segmentation algorithms in the past are based on optimizing an objective function that aims to achieve the similarity between several low-level features to build a partition of the image into homogeneous regions. In the present paper, we propose to incorporate the relevance (selection) of the grouping features to enforce the segmentation toward the capturing of objects of interest. The relevance of the features is determined through a set of positive and negative examples of a specific object defined a priori by the user. The calculation of the relevance of the features is performed by maximizing an objective function defined on the mixture likelihoods of the positive and negative object examples sets. The incorporation of the features relevance in the object segmentation is formulated through an energy functional which is minimized by using level set active contours. We show the efficiency of the approach on several examples of object of interest segmentation and tracking where the features relevance is used. © 2007 IEEE.},
note = {ISSN: 10636919},
keywords = {Feature extraction, Image acquisition, Image segmentation, Object recognition, Object segmentation, Objective functions, Optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
Most image segmentation algorithms in the past are based on optimizing an objective function that aims to achieve the similarity between several low-level features to build a partition of the image into homogeneous regions. In the present paper, we propose to incorporate the relevance (selection) of the grouping features to enforce the segmentation toward the capturing of objects of interest. The relevance of the features is determined through a set of positive and negative examples of a specific object defined a priori by the user. The calculation of the relevance of the features is performed by maximizing an objective function defined on the mixture likelihoods of the positive and negative object examples sets. The incorporation of the features relevance in the object segmentation is formulated through an energy functional which is minimized by using level set active contours. We show the efficiency of the approach on several examples of object of interest segmentation and tracking where the features relevance is used. © 2007 IEEE.