Deep Learning-Based Fastener Counting Method and Localization Correction Method

Jun Guo, Xiao Yang, Jie Lan, Jianwei Zou, Feilong Chen

Abstract: Low accuracy and wear are common issues with single photoelectric encoders in track positioning. A deep learning-based method for clip detection, counting, and positioning correction can effectively improve the accuracy of the photoelectric encoder. In this paper, we propose a subway track clip detection model based on MobileNetV3-YOLOv5s, a clip counting and positioning model based on DeepSort, and a fusion correction model for positioning data. Finally, through comparative experiments, we validate that our adopted method achieves higher positioning accuracy and stronger reliability.