Intelligent Pavement Diseases Detection Model based on YOLOv5s

Weihua Pan; Mengying Nie

doi:10.54097/3ATabQER

Authors

Weihua Pan
Mengying Nie

DOI:

https://doi.org/10.54097/3ATabQER

Keywords:

Target Detection, Pavement Diseases, Neural Network, Attention Mechanism

Abstract

There are many kinds of pavement diseases, and the detection background in the actual scene is complex, so the accuracy of the detection model is not high. Based on YOLOv5s, a pavement diseases detection model YOLOv5s-CBP is proposed, and the CBAM (Convolutional Block Attention Module) attention module was introduced into the backbone network to enhance the model's attention to the characteristics of pavement diseases. The BiFPN network was constructed in the neck, which realizes the cross-layer exchange and integration of information and retains more high-level semantic information. A small target detection layer P2 was added to the detection head to improve the detection ability of the model for small road diseases. The experimental results on data sets show that the precision and recall of the model are improved by 6.1% and 4.4% respectively, and the mAP is improved by 5.7%, which effectively improves the performance of the detection model.

Downloads

Download data is not yet available.

References

Ning Tingting. Research on pavement crack detection algorithm based on digital image processing [D]. Liaoning Normal University, 2023.

Chu Xiumin, Yan Xinping, Chen Xianqiao. Research on binarization method of pavement damage image [J]. Computer Engineering and Application, 2008(28):161-165.

Jiang Wei. Research and implementation of adaptive threshold segmentation algorithm for pavement crack diseases [D]. Nanjing University of Information Science and Technology, 2020.

Wang G, Peter W T, Yuan M. Automatic internal crack detection from a sequence of infrared images with a triple-threshold Canny edge detector[J]. Measurement Science and Technology, 2018, 29(2): 025403.

Rolling, Meng Hua, Liu Tangzhi. Research on edge detection algorithm of asphalt pavement crack image [J]. Northern Transportation, 2015(03):56-59.

Wang Wei, Meng Lina, Yan Zhaojin. Pavement damage detection based on regional growth and outermost edge tracking [J]. Journal of civil aviation university of china, 2016, 34(06):51-54.

Xiao Xiaoming, Ma Zhi, Cai Zixing et al. An Adaptive Region Growing Algorithm for Road Segmentation [J]. Control Engineering, 2011,18(03):364-368.

Chen Yimin, Li Wanyi, Weng Hanrui, et al. Overview of two-stage target detection algorithms based on deep learning [J]. Information and Computer (Theoretical Edition), 2023,35 (14): 112-114.

Zhu Hao, Zhou Shunyong, Liu Xue, et al. Overview of single-stage target detection algorithm based on deep learning [J]. Industrial Control Computer, 2023,36(04):101-103.

Kortmann F, Talits K, Fassmeyer P, et al. Detecting various road damage types in global countries utilizing faster r-cnn[C]//2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020: 5563-5571.

Nie M, Wang C. Pavement Crack Detection based on yolo v3[C]//2019 2nd international conference on safety produce informatization (IICSPI). IEEE, 2019: 327-330.

Yu Lu, Dai Tianjie, Yu Lihua. Intelligent detection of road diseases based on improved YOLOv5 [J]. journal of fujian university of technology, 2023,21(04):332-337.

Wang Linyi, Bai Jing, Li Wenjing et al. Research progress of YOLO series target detection algorithms [J]. Computer Engineering and Application, 2023,59(14):15-29.

Qi xuanhao, Zhi Min. a summary of attention mechanism in image processing [J/OL]. computer science and exploration: 1-20 [2023-10-16]. http://kns. cnki.net/ kcms/detail/ 11. 5602. tp.20629.10606060606

Tan M, Pang R, Le Q V. Efficientdet: Scalable and efficient object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 10781-10790.