Abstract
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. Experimental results indicate that the proposed method outperforms existing approaches across most evaluation metrics, achieving an mAP at least 1.5% higher than the baseline YOLOv11 and 5.3% higher than the representative two-stage method Faster-RCNN. Additionally, it demonstrates excellent adaptability to medium- and large-scale targets, providing an effective solution for intelligent city management and road maintenance.