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.
Yapi, D.; Allili, M. S.; Baaziz, N.
Automatic Fabric Defect Detection Using Learning-Based Local Textural Distributions in the Contourlet Domain Article de journal
Dans: IEEE Transactions on Automation Science and Engineering, vol. 15, no 3, p. 1014–1026, 2018, ISSN: 15455955 (ISSN), (Publisher: Institute of Electrical and Electronics Engineers Inc.).
Résumé | Liens | BibTeX | Étiquettes: Automatic Detection, Barium compounds, Bayes classifier (BC), Contourlet transform, Defect-detection systems, Defects, Fabric defect detection, Image decomposition, Learning-based approach, mixture of generalized Gaussians (MoGG), Statistical representations, Statistical signatures, Textile industry, texture analysis
@article{yapi_automatic_2018,
title = {Automatic Fabric Defect Detection Using Learning-Based Local Textural Distributions in the Contourlet Domain},
author = {D. Yapi and M. S. Allili and N. Baaziz},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018936989&doi=10.1109%2fTASE.2017.2696748&partnerID=40&md5=932ca98fca260daf5c4dbf9da642edbd},
doi = {10.1109/TASE.2017.2696748},
issn = {15455955 (ISSN)},
year = {2018},
date = {2018-01-01},
journal = {IEEE Transactions on Automation Science and Engineering},
volume = {15},
number = {3},
pages = {1014–1026},
abstract = {We propose a learning-based approach for automatic detection of fabric defects. Our approach is based on a statistical representation of fabric patterns using the redundant contourlet transform (RCT). The distribution of the RCT coefficients are modeled using a finite mixture of generalized Gaussians (MoGG), which constitute statistical signatures distinguishing between defective and defect-free fabrics. In addition to being compact and fast to compute, these signatures enable accurate localization of defects. Our defect detection system is based on three main steps. In the first step, a preprocessing is applied for detecting basic pattern size for image decomposition and signature calculation. In the second step, labeled fabric samples are used to train a Bayes classifier (BC) to discriminate between defect-free and defective fabrics. Finally, defects are detected during image inspection by testing local patches using the learned BC. Our approach can deal with multiple types of textile fabrics, from simple to more complex ones. Experiments on the TILDA database have demonstrated that our method yields better results compared with recent state-of-the-art methods.Note to Practitioners - Fabric defect detection is central to automated visual inspection and quality control in textile manufacturing. This paper deals with this problem through a learning-based approach. By opposite to several existing approaches for fabric defect detection, which are effective in only some types of fabrics and/or defects, our method can deal with almost all types of patterned fabric and defects. To enable both detection and localization of defects, a fabric image is first divided into local blocks, which are representative of the repetitive pattern structure of the fabric. Then, statistical signatures are calculated by modeling the distribution of coefficients of an RCT using the finite MoGG. The discrimination between defect-free and defective fabrics is then achieved through supervised classification of RCT-MoGG signatures based on expert-labeled examples of defective fabric images. Experiments have shown that our method yields very good performance in terms of defect detection and localization. In addition to its accuracy, inspection of images can be performed in a fully automatic fashion, whereas only labeled examples are initially required. Finally, our method can be easily adapted to a real-time scenario since defect detection on inspected images is performed at the block level, which can be easily parallelized through hardware implementation. © 2004-2012 IEEE.},
note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
keywords = {Automatic Detection, Barium compounds, Bayes classifier (BC), Contourlet transform, Defect-detection systems, Defects, Fabric defect detection, Image decomposition, Learning-based approach, mixture of generalized Gaussians (MoGG), Statistical representations, Statistical signatures, Textile industry, texture analysis},
pubstate = {published},
tppubtype = {article}
}
We propose a learning-based approach for automatic detection of fabric defects. Our approach is based on a statistical representation of fabric patterns using the redundant contourlet transform (RCT). The distribution of the RCT coefficients are modeled using a finite mixture of generalized Gaussians (MoGG), which constitute statistical signatures distinguishing between defective and defect-free fabrics. In addition to being compact and fast to compute, these signatures enable accurate localization of defects. Our defect detection system is based on three main steps. In the first step, a preprocessing is applied for detecting basic pattern size for image decomposition and signature calculation. In the second step, labeled fabric samples are used to train a Bayes classifier (BC) to discriminate between defect-free and defective fabrics. Finally, defects are detected during image inspection by testing local patches using the learned BC. Our approach can deal with multiple types of textile fabrics, from simple to more complex ones. Experiments on the TILDA database have demonstrated that our method yields better results compared with recent state-of-the-art methods.Note to Practitioners - Fabric defect detection is central to automated visual inspection and quality control in textile manufacturing. This paper deals with this problem through a learning-based approach. By opposite to several existing approaches for fabric defect detection, which are effective in only some types of fabrics and/or defects, our method can deal with almost all types of patterned fabric and defects. To enable both detection and localization of defects, a fabric image is first divided into local blocks, which are representative of the repetitive pattern structure of the fabric. Then, statistical signatures are calculated by modeling the distribution of coefficients of an RCT using the finite MoGG. The discrimination between defect-free and defective fabrics is then achieved through supervised classification of RCT-MoGG signatures based on expert-labeled examples of defective fabric images. Experiments have shown that our method yields very good performance in terms of defect detection and localization. In addition to its accuracy, inspection of images can be performed in a fully automatic fashion, whereas only labeled examples are initially required. Finally, our method can be easily adapted to a real-time scenario since defect detection on inspected images is performed at the block level, which can be easily parallelized through hardware implementation. © 2004-2012 IEEE.
3.
Yapi, D.; Mejri, M.; Allili, M. S.; Baaziz, N.
A learning-based approach for automatic defect detection in textile images Article d'actes
Dans: A., Zaremba M. Sasiadek J. Dolgui (Ed.): IFAC-PapersOnLine, p. 2423–2428, 2015, ISBN: 24058963 (ISSN), (Issue: 3 Journal Abbreviation: IFAC-PapersOnLine).
Résumé | Liens | BibTeX | Étiquettes: Algorithms, Artificial intelligence, Automatic defect detections, Barium compounds, Bayes Classifier, Computational efficiency, Contourlets, Defect detection, Defect detection algorithm, Defects, Detection problems, Feature extraction, Feature extraction and classification, Gaussians, Image classification, Learning algorithms, Learning systems, Learning-based approach, Machine learning approaches, Mixture of generalized gaussians, Mixtures of generalized Gaussians (MoGG), Textile defect detection, Textile images, Textiles, Textures
@inproceedings{yapi_learning-based_2015,
title = {A learning-based approach for automatic defect detection in textile images},
author = {D. Yapi and M. Mejri and M. S. Allili and N. Baaziz},
editor = {Zaremba M. Sasiadek J. Dolgui A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953865559&doi=10.1016%2fj.ifacol.2015.06.451&partnerID=40&md5=3dd0ef4c27cbd55700f6511af5f46772},
doi = {10.1016/j.ifacol.2015.06.451},
isbn = {24058963 (ISSN)},
year = {2015},
date = {2015-01-01},
booktitle = {IFAC-PapersOnLine},
volume = {28},
pages = {2423–2428},
abstract = {This paper addresses the textile defect detection problem using a machine-learning approach. We propose a novel algorithm that uses supervised learning to classify textile textures in defect and non-defect classes based on suitable feature extraction and classification. We use statistical modeling of multi-scale contourlet image decomposition to obtain compact and accurate signatures for texture description. Our defect detection algorithm is based on two phases. In the first phase, using a training set of images, we extract reference defect-free signatures for each textile category. Then, we use the Bayes classifier (BC) to learn signatures of defected and non-defected classes. In the second phase, defects are detected on new images using the trained BC and an appropriate decomposition of images into blocks. Our algorithm has the capability to achieve highly accurate defect detection and localisation in textile textures while ensuring an efficient computational time. Compared to recent state-of-the-art methods, our algorithm has yielded better results on the standard TILDA database. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},
note = {Issue: 3
Journal Abbreviation: IFAC-PapersOnLine},
keywords = {Algorithms, Artificial intelligence, Automatic defect detections, Barium compounds, Bayes Classifier, Computational efficiency, Contourlets, Defect detection, Defect detection algorithm, Defects, Detection problems, Feature extraction, Feature extraction and classification, Gaussians, Image classification, Learning algorithms, Learning systems, Learning-based approach, Machine learning approaches, Mixture of generalized gaussians, Mixtures of generalized Gaussians (MoGG), Textile defect detection, Textile images, Textiles, Textures},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper addresses the textile defect detection problem using a machine-learning approach. We propose a novel algorithm that uses supervised learning to classify textile textures in defect and non-defect classes based on suitable feature extraction and classification. We use statistical modeling of multi-scale contourlet image decomposition to obtain compact and accurate signatures for texture description. Our defect detection algorithm is based on two phases. In the first phase, using a training set of images, we extract reference defect-free signatures for each textile category. Then, we use the Bayes classifier (BC) to learn signatures of defected and non-defected classes. In the second phase, defects are detected on new images using the trained BC and an appropriate decomposition of images into blocks. Our algorithm has the capability to achieve highly accurate defect detection and localisation in textile textures while ensuring an efficient computational time. Compared to recent state-of-the-art methods, our algorithm has yielded better results on the standard TILDA database. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
4.
Allili, M. S.; Baaziz, N.; Mejri, M.
Texture modeling using contourlets and finite mixtures of generalized gaussian distributions and applications Article de journal
Dans: IEEE Transactions on Multimedia, vol. 16, no 3, p. 772–784, 2014, ISSN: 15209210, (Publisher: Institute of Electrical and Electronics Engineers Inc.).
Résumé | Liens | BibTeX | Étiquettes: Contourlet coefficients, Contourlet transform, Defects, Directional information, Fabric texture, face recognition, Generalized Gaussian Distributions, Inspection, Mixtures, Probability density function, Probability density functions (PDFs), State-of-the-art methods, Texture retrieval, Textures
@article{allili_texture_2014,
title = {Texture modeling using contourlets and finite mixtures of generalized gaussian distributions and applications},
author = {M. S. Allili and N. Baaziz and M. Mejri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896903467&doi=10.1109%2fTMM.2014.2298832&partnerID=40&md5=16b2fa741e71e1e581f6b0f54c43a676},
doi = {10.1109/TMM.2014.2298832},
issn = {15209210},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Multimedia},
volume = {16},
number = {3},
pages = {772–784},
abstract = {In this paper, we develop a new framework for contourlet-based statistical modeling using finite Mixtures of Generalized Gaussian distributions (MoGG). On the one hand, given the rich directional information provided by the contourlet transform (CT), we propose to use a redundant version of the CT, which describes texture structures more accurately. On the other hand, we use MoGG modeling of contourlet coefficients distribution, which allows for precise capturing of a wide range of histogram shapes and provides better description and discrimination of texture than single probability density functions (pdfs). Moreover, we propose three applications for the proposed approach, namely: (1) texture retrieval, (2) fabric texture defect detection, and 3) infrared (IR) face recognition. We compare two implementations of the CT: standard CT (SCT) and redundant CT (RCT). We show that the proposed approach yields better results in the applications studied compared to recent state-of-the-art methods. © 2014 IEEE.},
note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
keywords = {Contourlet coefficients, Contourlet transform, Defects, Directional information, Fabric texture, face recognition, Generalized Gaussian Distributions, Inspection, Mixtures, Probability density function, Probability density functions (PDFs), State-of-the-art methods, Texture retrieval, Textures},
pubstate = {published},
tppubtype = {article}
}
In this paper, we develop a new framework for contourlet-based statistical modeling using finite Mixtures of Generalized Gaussian distributions (MoGG). On the one hand, given the rich directional information provided by the contourlet transform (CT), we propose to use a redundant version of the CT, which describes texture structures more accurately. On the other hand, we use MoGG modeling of contourlet coefficients distribution, which allows for precise capturing of a wide range of histogram shapes and provides better description and discrimination of texture than single probability density functions (pdfs). Moreover, we propose three applications for the proposed approach, namely: (1) texture retrieval, (2) fabric texture defect detection, and 3) infrared (IR) face recognition. We compare two implementations of the CT: standard CT (SCT) and redundant CT (RCT). We show that the proposed approach yields better results in the applications studied compared to recent state-of-the-art methods. © 2014 IEEE.