Improved YOLOv10n Lightweight Road Crack Detection Model

NAVIGATE

Table of Contents

STATISTICS

Viewed91

Downloads79

Improved YOLOv10n Lightweight Road Crack Detection Model

PDF下载 (79)

[1]WANG Jingyang,XU Yongchao,ZHANG Bo,et al.Improved YOLOv10n Lightweight Road Crack Detection Model[J].Journal of Zhengzhou University (Engineering Science),2026,47(4):42-49.[doi:10.13705/j.issn.1671-6833.2026.04.007]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 Issue 4 Page number: 42-49 Column: Public date: 2026-07-10

Title:: Improved YOLOv10n Lightweight Road Crack Detection Model

Author(s):: WANG Jingyang¹, XU Yongchao¹, ZHANG Bo², WANG Jue³, HUANG Min¹; 1. School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China; 2. School of Cyberspace Security, Hebei University of Engineering Science, Shijiazhuang 050091, China; 3. Shijiazhuang Branch,Chi-naTelecom Corporation Limited, Shijiazhuang 050035, China

Keywords:: road crack detection; YOLOv10n; attention mechanism; loss function; lightweight

CLC:: TP391. 4U418. 6

DOI:: 10.13705/j.issn.1671-6833.2026.04.007

Abstract:: Aiming at the problem that the existing road crack detection model cannot effectively balance the detec-tionaccuracy, computational complexity and detection speed with poor practical application effect, a lightweight road crack detection model YOLO-CGVE based on improved YOLOv10n was proposed. Firstly, the coordinate at-tention(CA) module was used to replace the partial self-attention ( PSA) module to better capture the local and global relationships in space and improve the capacity to extract features. Secondly, the computational complexity was reduced by using lightweight GSConv to replace some standard convolutions in the backbone and neck net-works.Thirdly, the original C2f structure in the neck network was replaced by VoV-GSCSP, which allowed for the efficient merging of feature maps from various stages and further minimized computing complexity while maintaining accuracy. Finally, the ECIoU loss function was used to replace the original loss function to improve the detection box positioning accuracy and convergence speed. The experimental results on RDD2022_China dataset showed that compared with YOLOv10n, while keeping a high detection speed, the mAP@ 0. 5 of YOLO-CGVE was improved by 2. 4 percentage points, reaching 75. 9%, and the number of parameters and the amount of computation were de-creasedby 11. 1% and 9. 8%, respectively. YOLO-CGVE could better meet the application needs in environments with limited computing resources.

References:: [1] Sun Zhen, Zhu Lingxi, Qin Su, et al. Road surface defect detection algorithm based on YOLOv8[J]. Electronics, 2024, 13(12): 2413.
[2] Li Jun, Zhou Keyu, Zou Jun, et al. Protective equipment wearing detection algorithm in construction scenarios based on YOLOv8n[J]. Journal of Zhengzhou University (Engineering Science), 2025, 46(3): 19–25. [李军，周科宇，邹军，等. 基于改进YOLOv8n的施工场景下防护装备佩戴检测算法[J]. 郑州大学学报(工学版)，2025，46(3)：19–25.]
[3] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2014: 580–587.
[4] Ren Shaoqing, He Kaiming, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149.
[5] Yang Ziyi, Lan Xin, Wang Hui. Comparative analysis of YOLO series algorithms for UAV-based highway distress inspection: performance and application insights [J]. Sensors, 2025, 25(5): 1475.
[6] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2016: 779–788.
[7] Liu Wei, Anguelov D, Erhan D, et al. SSD: single shot MultiBox detector[C]//Computer Vision – ECCV 2016. Cham: Springer, 2016: 21–37.
[8] Wang Xueqiu, Gao Huanbing, Jia Zemeng. Improved road defect detection algorithm based on YOLOv8[J]. Computer Engineering and Applications, 2024, 60(17): 179–190. [王雪秋，高焕兵，郑泽萌. 改进YOLOv8的道路缺陷检测算法[J]. 计算机工程与应用，2024，60(17)：179–190.]
[9] Hu Fengkuo, Ye Lan, Tan Xianfeng, et al. A refined YOLOv8-based algorithm for lightweight pavement disease detection[J]. Journal of Graphics, 2024, 45(5): 892–900. [胡凤阔，叶兰，谭显峰，等. 一种基于改进YOLOv8的轻量化路面病害检测算法[J]. 图学学报，2024，45(5)：892–900.]
[10] Li Shanbin, Huang Yujun. Damage detection algorithm based on Faster-RCNN[C]//The 5th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT). Piscataway: IEEE, 2023: 177–180.
[11] Wang Jinlei, Meng Ruifeng, Huang Yuanhao, et al. Road defect detection based on improved YOLOv8s model[J]. Scientific Reports, 2024, 14: 16758.
[12] Youwai S, Chaiyaphat A, Chaipetch P. YOLO9tr: a lightweight model for pavement damage detection utilizing a generalized efficient layer aggregation network and attention mechanism[J]. Journal of Real-Time Image Processing, 2024, 21(5): 163.
[13] Carion N, Massa F, Synnaeve G, et al. End-to-end object detection with transformers[C]//Computer Vision – ECCV 2020. Cham: Springer, 2020: 213–229.
[14] Zhao Yan, Lyu Wenyu, Xu Shangliang, et al. DETRs beat YOLOs on real-time object detection [C]//2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2024: 16965–16974.
[15] Luo Xianglong, Liu Ruchen, He Xibin, et al. A road damage detection model based on improved RT-DETR for complex environments[J]. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 5037413.
[16] Wang Ao, Chen Hui, Liu Lihao, et al. YOLOv10: real-time end-to-end object detection [PP/OL]. V2. arXiv (2024-10-30)[2026-02-13]. https://arxiv.org/abs/2405.14458.
[17] Hou Qibin, Zhou Daquan, Feng Jiashi. Coordinate attention for efficient mobile network design[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2021: 13708–13717.
[18] Li Hulin, Li Jun, Wei Hanbing, et al. Slim-neck by GSConv: a lightweight-design for real-time detector architectures[J]. Journal of Real-Time Image Processing, 2024, 21(3): 62.
[19] Zheng Zhaohui, Wang Ping, Ren Dongwei, et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation[J]. IEEE Transactions on Cybernetics, 2022, 52(8): 8574–8586.
[20] Zhang Yifan, Ren Weiqiang, Zhang Zhang, et al. Focal and efficient IOU loss for accurate bounding box regression[J]. Neurocomputing, 2022, 506: 146–157.
[21] Chen Xinlin, Lian Qingwang, Chen Xuanlai, et al. Surface crack detection method for coal rock based on improved YOLOv5[J]. Applied Sciences, 2022, 12(19): 9695.
[22] Arya D, Maeda H, Ghosh S K, et al. RDD2022: a multi-national image dataset for automatic road damage detection[J]. Geoscience Data Journal, 2024, 11(4): 846–862.
[23] Yang Fan, Zhang Lei, Yu Sijia, et al. Feature pyramid and hierarchical boosting network for pavement crack detection[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(4): 1525–1535.
[24] Zhang Lei, Yang Fan, Daniel Zhang Y, et al. Road crack detection using deep convolutional neural network[C]//Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP). Piscataway: IEEE, 2016: 3708–3712.

Similar References:

Memo

Last Update: 2026-10-21