Journal of Guangxi Normal University(Natural Science Edition) ›› 2025, Vol. 43 ›› Issue (3): 84-97.doi: 10.16088/j.issn.1001-6600.2024071003

• Intelligence Information Processing • Previous Articles     Next Articles

Bridge Defect Detection Based on Data Augmentation and Improved YOLOv8

LIANG Yinjie, NAN Xinyuan*, CAI Xin, LI Yunpeng, GOU Haiguang   

  1. School of Electrical Engineering, Xinjiang University, Urumqi Xinjiang 830017, China
  • Received:2024-07-10 Revised:2024-09-06 Online:2025-05-05 Published:2025-05-14

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Abstract: In order to solve the problems of low detection accuracy, high missed detection rate and high false detection rate of bridge surface defects under the background of interference, a bridge defect detection method based on data enhancement and improved YOLOv8 is proposed. The small sample data is augmented by StyleGAN3 and depth image fusion. The SPD-Conv module is added to the YOLOv8 backbone to improve the feature extraction capability of low-resolution defects. Based on AFPN structure, AFPN_UCG structure is designed to make the network handle multi-scale information better. In C2f, RFCBAMConv and DLKA modules are introduced to construct C2f_RD module, which can transmit gradient information accurately and capture small target information more effectively. A new detection Head is designed by combining DCNv3 module with Dynamic Head, which combines three attention mechanisms of scale, space and task and uses DCNv3 dynamic adjustment to further improve the prediction performance of the model for irregular defects. Through experiments, mAP@0.5 increases by 2.4 percentage points after the data is expanded, and the accuracy rate of the improved YOLOv8 is 93.2% and mAP@0.5 is 91.3%, respectively, which are 4.2 and 4.3 percentage points higher than that of the original model, which can detect bridge defects more accurately.

Key words: bridge defect detection, StyleGAN3, YOLOv8, feature fusion, convolution of attention;information interactive

CLC Number:  TP391.41
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