Foreign object detection in coal mine belt transportation based on Fast_YOLOv3 algorithm

The existing foreign object detection methods in coal mine belt transportation are low in detection precision and slow in detection speed, and YOLOv3 algorithm has faster detection speed and higher detection precision. However, when it is used in foreign object detection in coal mine belt transportation, there are problems such as poor detection effect on small targets, easy to appear missing detection and imbalance of positive and negative samples. In order to solve the above problems, Fast_YOLOv3 algorithm is designed. By improving the priori box and bounding box, the algorithm is adapted to the detection scenario of small target foreign object in coal mine belt transportation. By adding the deconvolution network, the algorithm is able to improve the detection capability of small target foreign object. By introducing the Focal Loss to improve the cross entropy of the negative sample confidence in the loss function, the algorithm is able to solve the problem of imbalance in the number of positive and negative samples so as to improve the detection precision. The StiPic data enhancement method is designed to preprocess the coal belt transportation image to improve the training efficiency of the Fast_YOLOv3 model and the detection precision of small target foreign objects. The experimental and field test results show that the Fast_YOLOv3 algorithm can detect foreign objects in the belt transportation with an average precision of 90.12%, an average detection time of 35 ms, and a detection rate of 93.50% for small target foreign objects, which meets the requirements of foreign objects detection precision and real-time detection in the belt transportation field.