Superficial Defect Detection for Concrete Bridges Using YOLOv8 with Attention Mechanism and Deformation Convolution
Source abstract
Abstract
The accuracy of detecting superficial bridge defects using the deep neural network approach decreases significantly under light variation and weak texture conditions. To address these issues, an enhanced intelligent detection method based on the YOLOv8 deep neural network is proposed in this study. Firstly, multi-branch coordinate attention (MBCA) is proposed to improve the accuracy of coordinate positioning by introducing a global perception module in coordinate attention mechanism. Furthermore, a deformable convolution based on MBCA is developed to improve the adaptability for complex feature shapes. Lastly, the deformable convolutional network attention YOLO (DCNA-YOLO) detection algorithm is formed by replacing the deep C2F structure in the YOLOv8 architecture with a deformable convolution. A supervised dataset consisting of 4794 bridge surface damage images is employed to verify the proposed method, and the results show that it achieves improvements of 2.0% and 3.4% in mAP and R. Meanwhile, the model complexity decreases by 1.2G, increasing the detection speed by 3.5/f·s−1.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Industrial Vision Systems and Defect Detection · Non-Destructive Testing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Civil Engineering, Chongqing University, Chongqing 400045, China
Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Ministry of Education, Chongqing 400045, China
Funding
National Natural Science Foundation of China · Fundamental Research Funds for the Central Universities · 111 project of the Ministry of Education and the Bureau of Foreign Experts of China
References (31)
Show source-provided references
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