The coal port will produce dust in the process of unloading coal by the chute of the ship loader. In order to solve the problem of dust detection, this paper proposes a method of coal dust detection based on deep learning (YOLOv4-tiny). The improved YOLOv4-tiny network is used to train and test the dust data set of chute discharge. Because the detection algorithm cannot get the dust concentration, this paper divides the dust into four categories for detection, and finally counts the area of detection frames of the four categories of dust. After that, the dust concentration is approximately judged through the weighted sum calculation of these data. The experimental results show that the detection accuracy (AP) of four types of dust is 93.98%, 93.57%, 80.03% and 57.43%, the average detection accuracy (mAP) is 81.27% (which is close to 83.38% of YOLOv4), and the detection speed (FPS) is 25.1 (which is higher than 13.4 of YOLOv4). The algorithm can balance the speed and accuracy of dust detection, and can be used for real-time dust detection to improve the efficiency of suppressing coal dust generated by chute discharge.