Abstract:

In order to enhance the accuracy and reliability of coal gangue recognition, a coal gangue recognition network based on improved YOLO v4 is proposed, the initial anchoring frame is optimized using K-means++ clustering algorithm, the five convolution operations in PANet are replaced with CSP structure, and the pyramid structure of hole convolution is introduced at the same time to reduce the model parameters and realize the model light weight, adding a cross-connected edge to form a BiFPN structure to improve the detection capability of medium targets, and obtaining the My-YOLO v4 target detection model.

The proposed My-YOLO v4 recognition detection method is compared and analyzed with three detection methods, SSD, YOLO v3 and YOLO v4, by collecting mixed samples of coal and gangue in the field and using relevant experimental equipment. The experimental results show that detection algorithm detects coal mixed with gangue on the test set with mAP value of 98. 14% and FPS of 28. 3 frames/ second, which improves the recognition accuracy by 5. 41% and 2. 87% compared with SSD and YOLO v3 detection algorithms, respectively, and improves the recognition speed by 7. 7 frames/ second compared with YOLO v4 target detection model, by comparing The analysis of experimental data verifies the effective improvement of the overall performance of My-YOLO v4 target detection model.