Road manhole cover defect detection via multi-scale edge enhancement and feature aggregation pyramid

作者：Jing Liu, Jianyong Zhao, Yanyan Cao, Ying Wang, Chunyu Dong, Chaoping Guo · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-95450-8 · 被引用次数：5 · 研究领域：Infrastructure Maintenance and Monitoring、Image and Object Detection Techniques、Advanced Neural Network Applications

The safety and management efficiency of urban infrastructure are crucial in the urbanization process, and the rapid, precise identification of road manhole covers is essential for ensuring public safety and optimizing maintenance operations. However, the diverse shapes, materials, complex backgrounds, and visual similarities of road manhole cover defects pose significant challenges for object detection. Methods based on deep learning, while employing multi-scale pyramid structures for feature extraction, often overlook the visual similarity among different defect types and the subtle differences in edge features, leading to limited detection performance. This paper introduces an enhanced method, EEFA-YOLO, for defect detection in road manhole covers, incorporating two novel modules: the Multi-Scale Edge Enhancement (MSEE) and the Feature Aggregation Pyramid (FAP). The MSEE utilizes multi-scale feature extraction and edge information enhancement to improve the model's sensitivity to subtle objects and edge details. Meanwhile, the FAP leverages a feature aggregation and diffusion mechanism to ensure uniform contextual information across scales, effectively addressing issues related to scale variance and background interference. Additionally, we constructed a diverse dataset of road manhole covers across various scenarios and defect types, encompassing four categories: good, broken, lost, and misaligned, providing high-quality data support for algorithm training and validation. ...