Version 1
: Received: 22 May 2024 / Approved: 23 May 2024 / Online: 23 May 2024 (07:35:15 CEST)
How to cite:
Li, T.; Liu, G.; Tan, S. Superficial Defect Detection for Concrete Bridges Using YOLOv8 with Attention Mechanism and Deformation Convolution. Preprints2024, 2024051498. https://doi.org/10.20944/preprints202405.1498.v1
Li, T.; Liu, G.; Tan, S. Superficial Defect Detection for Concrete Bridges Using YOLOv8 with Attention Mechanism and Deformation Convolution. Preprints 2024, 2024051498. https://doi.org/10.20944/preprints202405.1498.v1
Li, T.; Liu, G.; Tan, S. Superficial Defect Detection for Concrete Bridges Using YOLOv8 with Attention Mechanism and Deformation Convolution. Preprints2024, 2024051498. https://doi.org/10.20944/preprints202405.1498.v1
APA Style
Li, T., Liu, G., & Tan, S. (2024). Superficial Defect Detection for Concrete Bridges Using YOLOv8 with Attention Mechanism and Deformation Convolution. Preprints. https://doi.org/10.20944/preprints202405.1498.v1
Chicago/Turabian Style
Li, T., Gang Liu and Shuaishuai Tan. 2024 "Superficial Defect Detection for Concrete Bridges Using YOLOv8 with Attention Mechanism and Deformation Convolution" Preprints. https://doi.org/10.20944/preprints202405.1498.v1
Abstract
The detection accuracy of bridge superficial defect using the deep neural network approach decreases significantly under light variation and weak texture condition. 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 (DAC-YOLO) detection algorithm is formed by replacing the deep C2F structure in the YOLOv8 architecture with deformable convolution. A supervised dataset consisting of 4794 bridge surface damage images is employed to verify the proposed method, and 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.
Keywords
concrete surface defects; Deep learning; YOLO; Attention mechanism; Deformable convolution
Subject
Engineering, Civil Engineering
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.
