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5 July 2024 Crack detection algorithm ES-YOLOX in complex backgrounds
Meng Zhu, Yuan Liu, YanQiang Li, Yong Wang, Qian Du
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Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131843Q (2024) https://doi.org/10.1117/12.3032913
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia
Abstract
Cracks are one of the most common damages in highway bridges, and timely detection of existing cracks is crucial for the safety of highway bridges. Targeting the issues of low crack detection accuracy and many missed detections in complex backgrounds, a new network ES-YOLOX (Embed-Shuffle-YOLOX) is proposed based on the YOLOX object detection algorithm. This method enhances the backbone network's capability for feature extraction by embedding additional CSPLayer, introducing the reconfigured ShuffleNetV2 structure, introducing depth-separable convolution to decrease both the parameter count and computational load of the network, and finally introducing the ECA attention module to make the network more concerned with crack features. Experimental results show that the proposed method increases the mAP50 value by 2.11 % and the F1 score by 2.17 %, and reduces the number of parameters and calculations by 55.8% and 62.7%.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Meng Zhu, Yuan Liu, YanQiang Li, Yong Wang, and Qian Du "Crack detection algorithm ES-YOLOX in complex backgrounds", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131843Q (5 July 2024); https://doi.org/10.1117/12.3032913
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KEYWORDS
Object detection
Convolution
Bridges
Deep learning
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