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Dynamic recognition and positioning of coal-carrying railway carriages based on binocular vision

TONG Zhixue, CHEN Jiajie, KANG Zhiqiang, QU Dongdong


(School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China)


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.


Key words: positioning of coal-carrying railway carriages; deep learning; binocular vision; object detection; YOLOv4-tiny


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基于双目视觉的载煤火车厢动态识别与定位


同志学, 陈佳杰, 康智强, 屈东东


(西安建筑科技大学 机电工程学院, 陕西 西安 710055)


摘要:载煤火车厢定位是煤炭采样机器人实现自主采样的关键技术之一。 针对载煤火车厢的动态识别与定位问题, 本文提出了一种双目立体视觉与改进YOLOv4-tiny结合的快速定位方法。 该方法将双目相机左视图RGB图像作为改进YOLOv4-tiny的输入, 得到载煤车厢在图像中的位置信息, 再结合双目定位原理计算出其空间位置。 改进后的算法主要对YOLOv4-tiny进行轻量化设计, 采用了新的激活函数, 并利用K-mans聚类算法重新生成样本先验锚框。 与原网络相比, 模型大小减少了62.5%, 准确率提高了0.92%, 召回率提高了0.89%, 检测速度提高了20%。 载煤火车厢在10 m以内的平均定位误差为4.1%, 最大误差为8.3%。 改进后的模型具有鲁棒性强、 识别速度快、 轻量化等优点, 能够实现载煤火车厢的动态识别及定位, 为煤炭采样机器人的自主采样提供了保证。 


关键词:载煤火车厢定位; 深度学习; 双目视觉; 目标检测; YOLOv4-tiny


引用格式:TONG Zhixue, CHEN Jiajie, KANG Zhiqiang, et al. Dynamic recognition and positioning of coal-carrying railway carriages based on binocular vision. Journal of Measurement Science and Instrumentation, 2023, 14(2): 148-155. DOI: 10.3969/j.issn.1674-8042.2023.02.003


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