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Deep Learning-Based Real-Time Crack Segmentation for Pavement Images
https://orcid.org/https://orcid.org/0000-0002-6018-7546
https://orcid.org/http://orcid.org/0000-0002-0027-7638
Crack is the early form of most pavement defects and has a great negative effect on road service life. Timely detection and maintenance of cracks may minimize the loss caused by it. In this paper, we propose a lightweight crack segmentation model based on a bilateral segmentation network, which achieves a good balance between inference speed and segmentation performance. The model contains two parts: context path and spatial path. The network used in context path is inspired by Xception, which is used to rapidly down-sample the feature map. Spatial path employs three convolutional layers to encode sufficient spatial information. The F1_score and IoU achieved by our model on the Crack500 dataset are 0.8270 and 0.7379, respectively. The proposed model gains superior performance in FPS compared to other four models. In addition, the model is able to process images at 1,024 × 512 pixels in real-time (31.3 FPS). Through the comparison of training time, our model can save 54.04% of the time.
Deep Learning-Based Real-Time Crack Segmentation for Pavement Images
https://orcid.org/https://orcid.org/0000-0002-6018-7546
https://orcid.org/http://orcid.org/0000-0002-0027-7638
Crack is the early form of most pavement defects and has a great negative effect on road service life. Timely detection and maintenance of cracks may minimize the loss caused by it. In this paper, we propose a lightweight crack segmentation model based on a bilateral segmentation network, which achieves a good balance between inference speed and segmentation performance. The model contains two parts: context path and spatial path. The network used in context path is inspired by Xception, which is used to rapidly down-sample the feature map. Spatial path employs three convolutional layers to encode sufficient spatial information. The F1_score and IoU achieved by our model on the Crack500 dataset are 0.8270 and 0.7379, respectively. The proposed model gains superior performance in FPS compared to other four models. In addition, the model is able to process images at 1,024 × 512 pixels in real-time (31.3 FPS). Through the comparison of training time, our model can save 54.04% of the time.
Deep Learning-Based Real-Time Crack Segmentation for Pavement Images
https://orcid.org/https://orcid.org/0000-0002-6018-7546
https://orcid.org/http://orcid.org/0000-0002-0027-7638
Crack is the early form of most pavement defects and has a great negative effect on road service life. Timely detection and maintenance of cracks may minimize the loss caused by it. In this paper, we propose a lightweight crack segmentation model based on a bilateral segmentation network, which achieves a good balance between inference speed and segmentation performance. The model contains two parts: context path and spatial path. The network used in context path is inspired by Xception, which is used to rapidly down-sample the feature map. Spatial path employs three convolutional layers to encode sufficient spatial information. The F1_score and IoU achieved by our model on the Crack500 dataset are 0.8270 and 0.7379, respectively. The proposed model gains superior performance in FPS compared to other four models. In addition, the model is able to process images at 1,024 × 512 pixels in real-time (31.3 FPS). Through the comparison of training time, our model can save 54.04% of the time.
Deep Learning-Based Real-Time Crack Segmentation for Pavement Images
KSCE J Civ Eng
Wang, Wenjun (author) / Su, Chao (author)
KSCE Journal of Civil Engineering ; 25 ; 4495-4506
2021-12-01
12 pages
Article (Journal)
Electronic Resource
English
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