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During the transfer process for coal with the utilization of mining conveyor belts, large pieces of coal often affect the safety of transportation, so real-time monitoring of the transport process in lump coal is essential. Therefore, a real-time monitoring method GSB YOLOv5 is proposed. First, the dataset&#x0027;s contrast is enhanced by adaptive histogram equalization, while its richness is improved by combining Mosaic multi-data enhancement. Second, it proposes the utilization of ghost net, which is a neural network with low computational requirements for lightweight extraction and fusion of features. This approach effectively minimizes the model computation. In addition, the combination of squeeze-excitation mechanism improves the extraction of model&#x0027;s feature capabilities. Finally, a feature pyramid with weighted bidirectional is employed to accomplish multisource information fusion by effectively merging features at varying resolutions. The experimental findings demonstrate that the enhanced GSB YOLOv5 algorithm achieves a 35.256&#x0025; significant reduction in network layers, while it has substantial reductions of 63.023&#x0025; and 68.582&#x0025; in parameters and floating point operations, respectively. Furthermore, the compressed model size is decreased from 92.7 to 34.4 MB. In addition, there are improvements of 1.421&#x0025; and 1.460&#x0025; in the detection precision and recall rate of the model, respectively, while the detection efficiency of the real-time has a significant boost from 68.34 to 107.91 FPS. Automatic, fast and high precision monitoring of lump coal objects on conveyor belts in underground coal mines can be realized.

A Lump Coal Detection Method Fusion of Lightweight and Attention Mechanism