Improved YOLOv10n Lightweight Road Crack Detection Model

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Improved YOLOv10n Lightweight Road Crack Detection Model

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[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(XX):1-8.[doi:10.13705/j.issn.1671-6833.2026.04.007]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 XX Page number: 1-8 Column: Public date: 2026-09-10

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,China Telecom 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 detection accuracy, computational complexity and detection speed and has poor practical application effect, a lightweight road crack detection model YOLO-CGVE based on improved YOLOv10n was proposed. Firstly, the coordinate attention (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 networks. Thirdly, the original C2f structure in the neck network was replaced by VoV-GSCP, 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 decreased by 11.1% and 9.8%, respectively. YOLO-CGVE could better meet the application needs in environments with limited computing resources.

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Last Update: 2026-02-27