@article{TONG2023, 
author = {Zhixue TONG and Jiajie CHEN and Zhiqiang KANG and Dongdong QU},
title = {Dynamic recognition and positioning of coal-carrying railway carriages based on binocular vision},
year = {2023},
journal = {Journal of Measurement Science and Instrumentation},
volume = {14},
number = {2},
pages = {148-155},
keywords = {positioning of coal-carrying railway carriages, deep learning, binocular vision, object detection, YOLOv4-tiny},
url = {https://www.sciopen.com/article/10.62756/jmsi.1674-8042.2023017},
doi = {10.62756/jmsi.1674-8042.2023017},
abstract = {The positioning of coal-carrying railway carriages is a critical technology for coal sampling robots to achieve autonomous sampling. A rapid positioning method for coal-carrying railway carriages combining binocular stereo vision and improved YOLOv4-tiny is proposed. The left view RGB image of the binocular camera is taken as the input of the improved YOLOv4-tiny. The position information of the coal-carrying railway car in the image is obtained. And its spatial distribution position is calculated based on the binocular positioning principle. The improved algorithm carries out a lightweight design for YOLOv4-tiny, adopts a new activation function, and uses the K-mans clustering algorithm to regenerate the prior anchor box. Compared with the original network, the model size has been reduced by 62.5%, the accuracy rate has been increased by 0.92%, the recall rate has been increased by 0.89%, and the detection speed has been increased by 20%. The average positioning error of the coal-carrying car within 10 m is 4.1%, and the maximum error is 8.3%. The improved model has the advantages of robustness, fast recognition speed, lightweight, etc., and can realize the dynamic recognition of the coal-carrying car. The positioning provides a guarantee for the autonomous sampling of coal sampling robots.}
}