Detection of Lump Coal based on Lightweight Network and Attention Mechanism

Safe operation of the conveyor belt is a necessary condition for the safe and efficient transport of coal production. In the process of transferring large pieces of coal, Due to the fast operation of conveyor belts in underground coal mines and the height difference at the interface, the impact force of large pieces of coal is large, which will cause damage to the conveyor belt. Aiming at the above problems, proposed a novel approach for real-time surveillance of the conveyor belt operation in large coal management. This method leverages the YOLOv5 algorithm to achieve efficient monitoring, which firstly preprocesses the data through adaptive histogram equalization, and secondly, introduces a lightweight neural network to reduce the volume and complexity of the model. Finally, the fusion of the attention mechanism improves the detection performance of the model, so as to realize the real-time monitoring of bulk coal. The experimental results show that the number of model parameters and floating-point operations of the improved algorithm are reduced by 62.8% and 86.4%, respectively, and the detection speed reaches 96.77fps, which has an obvious lightweight advantage, while the detection precision is 60.48%. Therefore, the real-time monitoring method of mining conveyor belt based on lightweight neural network and attention mechanism can better cope with the dim and complex environment of underground coal mine.