基于改进simAM-YOLOv8的路面多病害识别方法

doi:10.13229/j.cnki.jdxbgxb.20240586

Abstract

Abstract:

To solve the problems of multi-modal data and low recognition accuracy in road pavement distress detection， an improved pavement multi-distress recognition algorithm based on the YOLOv8 model enhanced with the non-parametric attention mechanism simAM is proposed. Utilizing the self-owned pavement distress dataset， Res2Net is embedded into the YOLOv8 structure to enhance multi-scale feature extraction capabilities while maintaining similar computational loads. The simAM module is employed to further adjust the weights of feature maps at different scales， improving the detection of targets. Genetic algorithm is used to increase the speed of automatic parameter searching for the model， and image enhancement techniques such as HSV and Mosaic are employed to expand the small sample distresss. Experimental results show that the improved simAM-YOLOv8 algorithm significantly improves accuracy and recall rates for various pavement distresss such as cracks， broken panels， repairs， etc.， on asphalt， cement， and other road surfaces. Specifically， the precision rate has increased by 15.3% and the recall rate has increased by 13.1% compared to the original network， demonstrating excellent intelligent recognition performance， and playing an important role in automated detection of highway conditions.

Key words: intelligent transportation, pavement distress, recognition algorithm, simAM, YOLOv8, Res2Net

CLC Number:

TP391.4

Fei SHAN,Hui LI,Hao SUN,Shi-gang NIE,Zhong-hu SHEN. Pavement distress identification method based on improved simAM-YOLOv8[J].Journal of Jilin University(Engineering and Technology Edition), 2026, 56(1): 219-230.

References 20

[1]	罗晖, 余俊英, 涂所成. 基于深度学习的公路路面病害检测算法[J]. 科学技术与工程, 2022, 22(13):5329-5305.
	Luo Hui, Yu Jun-ying, Tu Suo-cheng. Pavement defect detection algorithm based on deep learning[J]. Science Technology and Engineering, 2022, 22(13): 5329-5305.
[2]	Hou Y, Li Q, Zhang C, et al. The state-of-the-art review on applications of intrusive sensing, image processing techniques, and machine learning methods in pavement monitoring and analysis[J]. Engineering, 2021, 7(6): 845-856.
[3]	Cheng H, Wan F, Lei G,et al.GCS-YOLO: a lightweight strawberry disease detection algorithm based on improved YOLOv8[C]∥The 5th International Conference on Frontiers Technology of Information and Computer(ICFTIC), Qiangdao, China, 2023:1071-1076.
[4]	章世祥, 张汉成, 李西芝, 等. 基于机器视觉的路面裂缝病害多目标识别研究[J]. 公路交通科技, 2021, 38(3): 30-39.
	Zhang Shi-xiang, Zhang Han-cheng, Li Xi-zi,et al.Study on multi-objective identification of pavement cracks based on machine vision[J].Journal of Highway and Transportation Rresearch and Development,2021,38(3):30-39.
[5]	陈涵深. 基于深度学习的路面破损检测研究[D]. 杭州: 浙江工业大学信息工程学院, 2020.
	Chen Han-shen.Deep learning for pavement distress detection[D]. Hangzhou: College of Information Engineering,Zhejiang University of Technology, 2020.
[6]	魏陈浩, 杨睿, 刘振丙, 等. 具有双层路由注意力的YOLOv8道路场景目标检测方法[J]. 图学学报, 2023, 44(6): 1104-1111.
	Wei Chen-hao, Yang Rui, Liu Zhen-bing, et al. YOLOv8 with bi-level routing attention for road scene object detection[J]. Journal of Graphics, 2023, 44(6): 1104-1111.
[7]	侯越, 张慧婷, 高智伟, 等. 基于数据深度增强的路面病害智能检测方法研究及比较[J]. 北京工业大学学报, 2022, 48(6): 622-634.
	Hou Yue, Zhang Hui-ting, Gao Zhi-wei,et al.Research and comparison of intelligent detection methods of pavement distress based on deep data augmentation[J].Journal of Beijing University of Technology, 2022, 48(6): 622-634.
[8]	倪昌双, 李林, 罗文婷, 等. 改进YOLOv7的沥青路面病害检测[J]. 计算机工程与应用, 2023, 59(13): 305-316.
	Ni Chang-shuang, Li Lin, Luo Wen-ting, et al. Disease detection of asphalt pavement based on improved YOLOv7[J]. Computer Engineering and Applications, 2023, 59(13): 305-316.
[9]	田鹏, 毛力. 改进YOLOv8的道路交通标志目标检测算法[J]. 计算机工程与应用, 2024, 60(8): 202-212.
	Tian Peng, Mao Li. Improved YOLOv8 object detection algorithm for traffic sign target[J]. Computer Engineering and Applications, 2024, 60(8): 202-212.
[10]	Yang L, Zhang R Y, Li L, et al. Simam: a simple, parameter-free attention module for convolutional neural networks[C]∥International Conference On Machine Learning, Vienna, Austria, 2021: 11863-11874.
[11]	Xie J, Wu Z, Zhu R, et al. Melanoma detection based on swin transformer and SimAM[C]∥IEEE 5th Information Technology, Networking, Electronic and Automation Control Conference(ITNEC), Xi'an, China, 2021: 1517-1521.
[12]	韩强. 面向小目标检测的改进YOLOv8算法研究[D].长春: 吉林大学数学学院, 2024.
	Han Qiang. Research on improved YOLOv8 algorithm for small object detection[D].Changchun:School of Mathematics, Jilin University, 2024.
[13]	Du Y, Pan N, Xu Z, et al. Pavement distress detection and classification based on YOLO network[J]. International Journal of Pavement Engineering, 2021, 22(13): 1659-1672.
[14]	Jiang P, Ergu D, Liu F, et al. A review of YOLO algorithm developments[J]. Procedia Computer Science, 2022, 199: 1066-1073.
[15]	邹彦艳, 曹衍芬, 张馨月, 等. 基于 YOLOv8-DSG 的钢铁表面缺陷检测算法[J]. 吉林大学学报: 信息科学版, 2025, 43(1): 116-125
	Zou Yan-yan, Cao Yan-fen, Zhang Xin-yue, et al. Steel surface defect detection algorithm based on YOLOv8-DSG[J]. Journal of Jilin University(Information Science Edition), 2025, 43(1): 116-125.
[16]	Gao S H, Cheng M M, Zhao K, et al. Res2Net: a new multi-scale backbone architecture[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 43(2): 652-662.
[17]	Zhou Z, Wang Y, Feng C, et al. A moving ferromagnetic target localization method based on YOLO-V3[C]∥The 4th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP),Hangzhou,China,2022: 205-212.
[18]	李志慧, 钟涛, 赵永华, 等. 面向车辆自主驾驶的行人跟踪算法[J]. 吉林大学学报: 工学版, 2019, 49(3):680-687.
	Li Zhi-hui, Zhong Tao, Zhao Yong-hua, et al. Pedestrian tracking algorithm for autonomous vehicle driving[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 680-687.
[19]	宗芳, 齐厚成, 唐明, 等. 基于GPS数据的日出行模式-出行目的识别[J]. 吉林大学学报: 工学版, 2018, 48(5): 1374-1379.
	Zong Fang, Qi Hou-cheng, Tang Ming, et al. Daily travel mode based on GPS data - travel purpose recognition[J]. Journal of Jilin University(Engineering and Technology Edition),2018, 48(5):1374-1379..
[20]	Arya D, Maeda H, Ghosh S K, et al.Rdd2022: a multi-national image dataset for automatic road damage detection[DB/OL].[2024-04-26]..

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Pavement distress identification method based on improved simAM-YOLOv8

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