Title: A high accurate and fast road crack detection algorithm based on YOLOv8

Authors: Baishao Zhan; Xiong Zhou; Qiangqiang Zeng; Zhizhong Tan; Zhangwei Guo; Wei Luo; Hailiang Zhang

Addresses: East China Jiaotong University (ECJTU), Nanchang, Jiangxi, China ' East China Jiaotong University (ECJTU), Nanchang, Jiangxi, China ' East China Jiaotong University (ECJTU), Nanchang, Jiangxi, China ' East China Jiaotong University (ECJTU), Nanchang, Jiangxi, China ' East China Jiaotong University (ECJTU), Nanchang, Jiangxi, China ' East China Jiaotong University (ECJTU), Nanchang, Jiangxi, China ' East China Jiaotong University (ECJTU), Nanchang, Jiangxi, China

Abstract: In order to solve the problem of balancing the detection accuracy and speed in the existing road crack detection algorithms, this paper proposes an improved algorithm based on YOLOv8, named RPDD. First, we use the RepViT module to replace the original backbone network structure, which enhances the feature extraction capability. Second, we employ DySample up-sampling in the neck structure, which significantly improves the processing capability for low-resolution images. Finally, we change the detection head to a novel DyHead structure and combine it with deformable convolution (DCNV4), which enhances the adaptability to various scales and complex scenes. The experimental results show that RPDD achieves significant progress compared to the baseline, including 6.8% increase in precision, 4.6% increase in recall, 7.6% increase in mAP@0.5, and 72.9 increase in FPS, which validates its effectiveness in improving detection accuracy and speed.

Keywords: road crack detection; YOLOv8; RepViT; DySample; DyHead.

DOI: 10.1504/IJWMC.2025.148587

International Journal of Wireless and Mobile Computing, 2025 Vol.29 No.3, pp.270 - 282

Received: 01 Oct 2024
Accepted: 09 Dec 2024
Published online: 14 Sep 2025 *

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