Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Amirkhani, D.; Allili, M. S.; Hebbache, L.; Hammouche, N.; Lapointe, J.
Visual Concrete Bridge Defect Classification and Detection Using Deep Learning: A Systematic Review Article de journal
Dans: IEEE Transactions on Intelligent Transportation Systems, p. 1–23, 2024, ISSN: 15249050, (Publisher: Institute of Electrical and Electronics Engineers Inc.).
Résumé | Liens | BibTeX | Étiquettes: Annotation, Annotations, Bridges, Classification, Concrete, Concrete bridge defect, Concrete bridge defects, Concrete bridges, Concrete defects, Concretes, Deep learning, Defect classification, Defect detection, Defects, Detection, Inspection, Reviews, Segmentation, Taxonomies, Visualization
@article{amirkhani_visual_2024,
title = {Visual Concrete Bridge Defect Classification and Detection Using Deep Learning: A Systematic Review},
author = {D. Amirkhani and M. S. Allili and L. Hebbache and N. Hammouche and J. Lapointe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186994244&doi=10.1109%2fTITS.2024.3365296&partnerID=40&md5=a9228252d620ad6d444cc395296ebac2},
doi = {10.1109/TITS.2024.3365296},
issn = {15249050},
year = {2024},
date = {2024-01-01},
journal = {IEEE Transactions on Intelligent Transportation Systems},
pages = {1–23},
abstract = {Visual inspection is an important process for maintaining bridges in road transportation systems, and preventing catastrophic events and tragedies. In this process, accurate and automatic concrete defect classification and detection are major components to ensure early identification of any issue that can compromise the bridge safety and integrity. While a tremendous body of research has been proposed in the last decades for addressing these problems, the advent of deep learning unleashed huge opportunities for building more accurate and efficient methods. Our aim in this survey is to study the recent progress of vision-based concrete bridge defect classification and detection in the deep learning era. Our review encompasses major aspects underlying typical frameworks, which include concrete defect taxonomy, public datasets and evaluation metrics. We provide also a taxonomy of deep-learning-based classification and detection algorithms with a detailed discussion of their advantages and limitations. We also benchmark baseline models for classification and detection, using two popular datasets. We finally discuss important challenges of concrete defect classification and detection, and promising research avenues to build better models and integrate them in real-world visual inspection systems, which warrant further scientific investigation. IEEE},
note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
keywords = {Annotation, Annotations, Bridges, Classification, Concrete, Concrete bridge defect, Concrete bridge defects, Concrete bridges, Concrete defects, Concretes, Deep learning, Defect classification, Defect detection, Defects, Detection, Inspection, Reviews, Segmentation, Taxonomies, Visualization},
pubstate = {published},
tppubtype = {article}
}
Visual inspection is an important process for maintaining bridges in road transportation systems, and preventing catastrophic events and tragedies. In this process, accurate and automatic concrete defect classification and detection are major components to ensure early identification of any issue that can compromise the bridge safety and integrity. While a tremendous body of research has been proposed in the last decades for addressing these problems, the advent of deep learning unleashed huge opportunities for building more accurate and efficient methods. Our aim in this survey is to study the recent progress of vision-based concrete bridge defect classification and detection in the deep learning era. Our review encompasses major aspects underlying typical frameworks, which include concrete defect taxonomy, public datasets and evaluation metrics. We provide also a taxonomy of deep-learning-based classification and detection algorithms with a detailed discussion of their advantages and limitations. We also benchmark baseline models for classification and detection, using two popular datasets. We finally discuss important challenges of concrete defect classification and detection, and promising research avenues to build better models and integrate them in real-world visual inspection systems, which warrant further scientific investigation. IEEE
2.
Allili, M. S.; Ziou, D.
A Bayesian approach for weighting boundary and region information for segmentation Article de journal
Dans: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3708 LNCS, p. 468–475, 2005, ISSN: 03029743, (ISBN: 354029032X; 9783540290322 Place: Antwerp).
Résumé | Liens | BibTeX | Étiquettes: Adaptive systems, Bayesian approach, Boundary localization, Boundary value problems, decision making, Image segmentation, Lighting, Parameter estimation, Segmentation, Textures, Variational image segmentation
@article{allili_bayesian_2005,
title = {A Bayesian approach for weighting boundary and region information for segmentation},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33646174288&doi=10.1007%2f11558484_59&partnerID=40&md5=e98b02fbcf69f8acda0b171ce009d5ff},
doi = {10.1007/11558484_59},
issn = {03029743},
year = {2005},
date = {2005-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {3708 LNCS},
pages = {468–475},
abstract = {Variational image segmentation combining boundary and region information was and still is the subject of many recent works. This combination is usually subject to arbitrary weighting parameters that control the boundary and region features contribution during the segmentation. However, since the objective functions of the boundary and the region features is different in nature, their arbitrary combination may conduct to local conflicts that stem principally from abrupt illumination changes or the presence of texture inside the regions. In the present paper, we investigate an adaptive estimation of the weighting parameters (hyper-parameters) on the regions data during the segmentation by using a Bayesian method. This permits to give adequate contributions of the boundary and region features to segmentation decision making for pixels and, therefore, improving the accuracy of region boundary localization. We validated the approach on examples of real world images. © Springer-Verlag Berlin Heidelberg 2005.},
note = {ISBN: 354029032X; 9783540290322
Place: Antwerp},
keywords = {Adaptive systems, Bayesian approach, Boundary localization, Boundary value problems, decision making, Image segmentation, Lighting, Parameter estimation, Segmentation, Textures, Variational image segmentation},
pubstate = {published},
tppubtype = {article}
}
Variational image segmentation combining boundary and region information was and still is the subject of many recent works. This combination is usually subject to arbitrary weighting parameters that control the boundary and region features contribution during the segmentation. However, since the objective functions of the boundary and the region features is different in nature, their arbitrary combination may conduct to local conflicts that stem principally from abrupt illumination changes or the presence of texture inside the regions. In the present paper, we investigate an adaptive estimation of the weighting parameters (hyper-parameters) on the regions data during the segmentation by using a Bayesian method. This permits to give adequate contributions of the boundary and region features to segmentation decision making for pixels and, therefore, improving the accuracy of region boundary localization. We validated the approach on examples of real world images. © Springer-Verlag Berlin Heidelberg 2005.
3.
Allili, M. S.; Ziou, D.
Automatic change detection and updating of topographic databases by using satellite imagery: A level set approach Article de journal
Dans: Geomatica, vol. 59, no 3, p. 275–281, 2005, ISSN: 11951036.
Résumé | Liens | BibTeX | Étiquettes: Automatic change detection, Database systems, detection method, Edge detection, Feature extraction, Geographic information systems, Image analysis, Image segmentation, Imaging techniques, Landsat thematic mapper, Mathematical models, Probability, Remote sensing, Satellite imagery, Segmentation, spatial data, Topographic databases, topographic mapping
@article{allili_automatic_2005-2,
title = {Automatic change detection and updating of topographic databases by using satellite imagery: A level set approach},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-28444481044&partnerID=40&md5=e5d147401a402b14084292a2eeb6a792},
issn = {11951036},
year = {2005},
date = {2005-01-01},
journal = {Geomatica},
volume = {59},
number = {3},
pages = {275–281},
abstract = {In order to keep up-to-date geospatial data in topographic databases, automatic change detection and data updating is required. In the present paper, we investigate the automatic change detection of geospatial data by using level set active contours. We propose an approach that is based on region comparison between two multi-temporal datasets. Firstly, the regions are extracted from two co-registered images taken apart in time by using level set based active contours segmentation. Then, the change detection is performed by spatially comparing the resulting region segments from the two images. The approach is validated by experiments relating to the change detection of lake surfaces by using Landsat7 multi-spectral imagery.},
keywords = {Automatic change detection, Database systems, detection method, Edge detection, Feature extraction, Geographic information systems, Image analysis, Image segmentation, Imaging techniques, Landsat thematic mapper, Mathematical models, Probability, Remote sensing, Satellite imagery, Segmentation, spatial data, Topographic databases, topographic mapping},
pubstate = {published},
tppubtype = {article}
}
In order to keep up-to-date geospatial data in topographic databases, automatic change detection and data updating is required. In the present paper, we investigate the automatic change detection of geospatial data by using level set active contours. We propose an approach that is based on region comparison between two multi-temporal datasets. Firstly, the regions are extracted from two co-registered images taken apart in time by using level set based active contours segmentation. Then, the change detection is performed by spatially comparing the resulting region segments from the two images. The approach is validated by experiments relating to the change detection of lake surfaces by using Landsat7 multi-spectral imagery.