YOLO-CSMD: Integrating Improved Convolutional Techniques for Manhole Cover Defect Detection
Year: 2025
Type: article
Abstract: Recent detection algorithms for manhole cover defects exhibit limited detection capabilities, which frequently encounter the issue of missed detection. To address this issue, this paper proposes an improved detection algorithm based on YOLOv8, called YOLO-CSMD. First, Convolution State Space Module (ConvSSM) was introduced into the Neck network to improve our model’s sequence modeling capabilities. Second, the Spatial Texture Transform Module (STTM) is proposed to improve the ability of texture processing and feature fusion through the lightweight convolution and efficient multiscale attention module. Finally, the experimental results of YOLO-CSMD, using YOLOv8 as the baseline on our self-build dataset which including 5 defect classes and 10,115 images show that the mAP@50 of our model increased from 78.1% to 84.8%, and the mAP@50-95 increased from 61.3% to 67.5%. This demonstrates the effectiveness of our method.
Source: IEEE Access
Authors: Zhiwang Xu, Haowei Luo, Huijie Zhu, Wanfa Sun, Shengying Yang
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4411270171