Research on intelligent detection of coal gangue based on deep learning

In this paper, YOLOv4 algorithm based on deep learning is used to detect coal gangue. Firstly, the data set of coal gangue was made, which provides sufficient data for the training and verification of the detection algorithm model. Then, the coal gangue data set was used to test the influence of the combined use of optimization methods on the YOLOv4 detection algorithm. Finally, the performance of YOLOv4, SSD and Faster R-CNN detection algorithms combined with optimization methods in the field of coal gangue detection was compared through the coal gangue test data sets and the detection experiments. According to the coal gangue test data sets and coal gangue detection experiments, the combined use of optimization methods results in the mAP value of the YOLOv4 detection algorithm reaching 97.52%, which is 40.70% and 43.81% higher than those of the SSD and Faster R-CNN detection algorithms, respectively. Moreover, the accuracy, recall rate, and real-time performance of the YOLOv4 detection algorithm with the optimization methods are also better than those of the SSD and Faster R-CNN detection algorithms.

Automated summary

In this paper, the YOLOv4 algorithm based on deep learning is applied to detect coal gangue, and the combined use of optimization methods is shown to improve the algorithm's performance. Through comparisons with coal gangue test data sets and detection experiments, it is found that the YOLOv4 algorithm performs excellently in the field of coal gangue detection, and the use of optimization methods further enhances its performance.