부산대학교 도서관

Fast detection of fasteners is important to improve the efficiency of railroad maintenance. However, this task remains challenging due to the limited computing resources of inspection system. To solve the challenge, a new lightweight detection network Op-YOLOv4-tiny is proposed in this paper. The proposed network firstly uses ResBlock-N modules to replace the CSPBlock modules in YOLOv4-tiny to reduce the computation complexity. Then, a large scale feature map ( $52\times 52$ ) is added to obtain more features of fasteners to improve the detection accuracy. Extensive experiments are conducted on the captured railway and subway track images and the results show that Op-YOLOv4-tiny has good performance in terms of detection accuracy and speed. In detail, the detection speed and accuracy reach 408 FPS and 96.8%, respectively. In addition, compared with other detection networks and state-of-the-arts, it achieves the better performance. Thus, our proposed Op-YOLOv4-tiny is with some potential industrial application value for fast detection of fasteners.