Research on bridge surface defects based on YOLOv7

Qinghe Jiang; Hui Zhang; Hongyan Zhang; Wenping Jiang

doi:10.1117/12.3031149

19 July 2024 Research on bridge surface defects based on YOLOv7

Qinghe Jiang, Hui Zhang, Hongyan Zhang, Wenping Jiang

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131813S (2024) https://doi.org/10.1117/12.3031149
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

This paper explores deep learning methods for bridge surface defect detection, particularly an improved model based on YOLOv7. To address the detection of bridge-type defects, a Dual-Stream Attention Module (DSAM) and a Hybrid Atrous Pyramid Module (DP) were introduced, aiming to enhance the model's capability to capture key features and the efficiency of multi-scale feature extraction. Experimental results show that the improved model demonstrates higher detection accuracy on a bridge defect dataset, with the YOLOv7+DSAM+DP model achieving a mean Average Precision (mAP) of 91.3%. Additionally, the study compares the SSD and Faster R-CNN networks, confirming the superiority of the proposed model. Overall, the model presented in this paper significantly improves the precision and efficiency of bridge surface defect detection by integrating attention mechanisms and hybrid atrous pyramid modules.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qinghe Jiang, Hui Zhang, Hongyan Zhang, and Wenping Jiang "Research on bridge surface defects based on YOLOv7", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131813S (19 July 2024); https://doi.org/10.1117/12.3031149

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KEYWORDS

Bridges

Convolution

Defect detection

Feature extraction

Data modeling

Deep convolutional neural networks

Detection and tracking algorithms

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