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 Journal Article
In: Remote Sensing, vol. 15, no. 5, 2023, ISSN: 20724292, (Publisher: MDPI).
Abstract | Links | BibTeX | Tags: 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.
Chagnon-Forget, M.; Rouhafzay, G.; Cretu, A. -M.; Bouchard, S.
Enhanced Visual-Attention Model for Perceptually Improved 3D Object Modeling in Virtual Environments Journal Article
In: 3D Research, vol. 7, no. 4, 2016, ISSN: 20926731 (ISSN), (Publisher: 3D Display Research Center).
Abstract | Links | BibTeX | Tags: 3-d modeling, 3D modeling, Behavioral research, Complex networks, Interest points, Level of detail, Mesh simplification, Mesh simplifications, Saliency, Three dimensional computer graphics, virtual reality, Visual Attention
@article{chagnon-forget_enhanced_2016,
title = {Enhanced Visual-Attention Model for Perceptually Improved 3D Object Modeling in Virtual Environments},
author = {M. Chagnon-Forget and G. Rouhafzay and A. -M. Cretu and S. Bouchard},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991727567&doi=10.1007%2fs13319-016-0106-7&partnerID=40&md5=136feba48a1aa3ff798619f8d734c956},
doi = {10.1007/s13319-016-0106-7},
issn = {20926731 (ISSN)},
year = {2016},
date = {2016-01-01},
journal = {3D Research},
volume = {7},
number = {4},
abstract = {Three-dimensional object modeling and interactive virtual environment applications require accurate, but compact object models that ensure real-time rendering capabilities. In this context, the paper proposes a 3D modeling framework employing visual attention characteristics in order to obtain compact models that are more adapted to human visual capabilities. An enhanced computational visual attention model with additional saliency channels, such as curvature, symmetry, contrast and entropy, is initially employed to detect points of interest over the surface of a 3D object. The impact of the use of these supplementary channels is experimentally evaluated. The regions identified as salient by the visual attention model are preserved in a selectively-simplified model obtained using an adapted version of the QSlim algorithm. The resulting model is characterized by a higher density of points in the salient regions, therefore ensuring a higher perceived quality, while at the same time ensuring a less complex and more compact representation for the object. The quality of the resulting models is compared with the performance of other interest point detectors incorporated in a similar manner in the simplification algorithm. The proposed solution results overall in higher quality models, especially at lower resolutions. As an example of application, the selectively-densified models are included in a continuous multiple level of detail (LOD) modeling framework, in which an original neural-network solution selects the appropriate size and resolution of an object. © 2016, 3D Research Center, Kwangwoon University and Springer-Verlag Berlin Heidelberg.},
note = {Publisher: 3D Display Research Center},
keywords = {3-d modeling, 3D modeling, Behavioral research, Complex networks, Interest points, Level of detail, Mesh simplification, Mesh simplifications, Saliency, Three dimensional computer graphics, virtual reality, Visual Attention},
pubstate = {published},
tppubtype = {article}
}
Three-dimensional object modeling and interactive virtual environment applications require accurate, but compact object models that ensure real-time rendering capabilities. In this context, the paper proposes a 3D modeling framework employing visual attention characteristics in order to obtain compact models that are more adapted to human visual capabilities. An enhanced computational visual attention model with additional saliency channels, such as curvature, symmetry, contrast and entropy, is initially employed to detect points of interest over the surface of a 3D object. The impact of the use of these supplementary channels is experimentally evaluated. The regions identified as salient by the visual attention model are preserved in a selectively-simplified model obtained using an adapted version of the QSlim algorithm. The resulting model is characterized by a higher density of points in the salient regions, therefore ensuring a higher perceived quality, while at the same time ensuring a less complex and more compact representation for the object. The quality of the resulting models is compared with the performance of other interest point detectors incorporated in a similar manner in the simplification algorithm. The proposed solution results overall in higher quality models, especially at lower resolutions. As an example of application, the selectively-densified models are included in a continuous multiple level of detail (LOD) modeling framework, in which an original neural-network solution selects the appropriate size and resolution of an object. © 2016, 3D Research Center, Kwangwoon University and Springer-Verlag Berlin Heidelberg.