TY  - JOUR
PY  - 2023//
TI  - Real-time detection of road manhole covers with a deep learning model
JO  - Scientific reports
A1  - Pang, Dangfeng
A1  - Guan, Zhiwei
A1  - Luo, Tao
A1  - Su, Wei
A1  - Dou, Ruzhen
SP  - e16479
EP  - e16479
VL  - 13
IS  - 1
N2  - Road 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2045-2322
UR  - http://dx.doi.org/10.1038/s41598-023-43173-z
ID  - ref1
ER  -