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.