A lightweight method for real-time monitoring of lump coal on mining conveyor belts

During the transfer of coal using mining conveyor belts, broken coal and lump coal are typically present, along with a few larger pieces of coal. These larger pieces can pose a safety risk during transportation, making it crucial to monitor the lump coal in real-time throughout the process. To solve the above problems, this paper proposes the real-time monitoring method Ghost-SE-Bi FPN (GSB) YOLOv5. First, for data pre-processing, we enhance the contrast of the dataset using adaptive histogram equalization. To further enrich the dataset, we combine Mosaic multi-data enhancement techniques. Second, in this paper, we introduce Ghost Net, a lightweight neural network that performs feature extraction and fusion. This reduces the computational complexity of the model while maintaining its performance. We also incorporate the Squeeze-Excitation attention mechanism to improve feature extraction and weight adjustment, which accelerates model convergence. Finally, in order to efficiently complete multi-source information fusion, a weighted bidirectional feature pyramid is employed to fuse features of varying resolutions. According to the experimental results, the GSB YOLOv5 algorithm has been improved to reduce the number of network layers by 35.256%. Furthermore, the number of parameters and floating point operations have been reduced by 63.023% and 68.582%, respectively. The model size is compressed from 92.7M to 34.4M. In terms of detection performance, the precision and recall rate of the model detection improved by 1.421% and 1.460%, respectively. Additionally, the real-time detection efficiency increased from 68.34 FPS to 107.91 FPS. These findings demonstrate that GSB YOLOv5 not only achieves lightweight, but also effectively enhances various detection performance of the model.