Real-time detection of road manhole covers with a deep learning model

Abstract

AbstractRoad manhole covers are crucial components of urban infrastructure; however, inadequate maintenance or poor marking can pose safety risks to vehicular traffic. This paper presents a method for detecting road manhole covers using a stereo depth camera and the MGB-YOLO model. We curated a robust image dataset and performed image enhancement and annotation. The MGB-YOLO model was developed by optimizing the YOLOv5s network with MobileNet-V3, Global Attention Mechanism (GAM), and BottleneckCSP, striking a balance between detection accuracy and model efficiency. Our method achieved an impressive accuracy of 96.6%, surpassing the performance of Faster RCNN, SSD, YOLOv5s, YOLOv7 and YOLOv8s models with an increased mean average precision (mAP) of 15.6%, 6.9%, 0.7%, 0.5% and 0.5%, respectively. Additionally, we have reduced the model's size and the number of parameters, making it highly suitable for deployment on in-vehicle embedded devices. These results underscore the effectiveness of our approach in detecting road manhole covers, offering valuable insights for vehicle-based manhole cover detection and contributing to the reduction of accidents and enhanced driving comfort.