Version 1
: Received: 8 March 2024 / Approved: 12 March 2024 / Online: 12 March 2024 (08:18:47 CET)
How to cite:
Sun, L.; Liu, Z.; Xie, Z. Crack Detection in Orthographic Road Images Based on EC-YOLOX Algorithm. Preprints2024, 2024030680. https://doi.org/10.20944/preprints202403.0680.v1
Sun, L.; Liu, Z.; Xie, Z. Crack Detection in Orthographic Road Images Based on EC-YOLOX Algorithm. Preprints 2024, 2024030680. https://doi.org/10.20944/preprints202403.0680.v1
Sun, L.; Liu, Z.; Xie, Z. Crack Detection in Orthographic Road Images Based on EC-YOLOX Algorithm. Preprints2024, 2024030680. https://doi.org/10.20944/preprints202403.0680.v1
APA Style
Sun, L., Liu, Z., & Xie, Z. (2024). Crack Detection in Orthographic Road Images Based on EC-YOLOX Algorithm. Preprints. https://doi.org/10.20944/preprints202403.0680.v1
Chicago/Turabian Style
Sun, L., Zeyu Liu and Zhiwei Xie. 2024 "Crack Detection in Orthographic Road Images Based on EC-YOLOX Algorithm" Preprints. https://doi.org/10.20944/preprints202403.0680.v1
Abstract
Pavement crack detection is one of the key links in highway pavement maintenance management. In the current road crack detection, the network model easily ignores the shallow geometric features of cracks and cannot extract the key feature information of cracks. Aiming at the above problems, an EC-YOLOX network model is proposed. The CFPN is constructed to cross-fuse different scale features to solve the feature scale invariance. In the strengthen feature extraction layer, ECANet is fused to enhance the ability to pay attention to key information. The WEIoU loss function is proposed, which assigns different penalty terms to the target box in the vertical and horizontal directions. In terms of data, geometric distortion constraint correction is performed on the single-point perspective pavement image. The experimental data show that the accuracy of EC-YOLOX on the self-built data set reaches 76.34% mAP@0.5, which is 4.45% higher than that of YOLOX. The loss curve of EC-YOLOX is smoother than that of YOLOX, and the minimum training loss value is 2.136, which is 0.648 lower than the minimum loss value 2.784 of YOLOX. Many experiments have verified the effectiveness of EC-YOLOX in improving the detection effect in pavement crack detection.
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
