Intelligent safety monitoring algorithm for mining conveyor belts based on lightweight deep learning

The Mining conveyor belts play a vital role in mining production, however, due to their long time and high load operation characteristics, there are inevitably a variety of safety hazards. Ensuring the safety of mining conveyor belts is crucial to minimizing accidents and enhancing production efficiency. This study outlines the importance of safety surveillance for mining conveyor belts and introduces the LEC YOLOv5 algorithm. Building upon the YOLOv5 algorithm, the proposed approach integrates an innovative network, attention mechanism, early stopping mechanism, and optimized loss function. Initially, the Ghost convolution method is introduced to simplify the model’s complexity and structure. Additionally, the Efficient Channel Attention mechanism is incorporated to enhance the model’s detection performance. Subsequently, an early stop mechanism is implemented to decrease training time, hardware consumption, and overall model computation. Finally, the loss function is refined to strengthen the model’s localization ability. Experimental results demonstrate that the enhanced LEC YOLOv5 algorithm decreases the number of model parameters and floating-point operations by approximately 44.03% and 50.42%, respectively. Moreover, the model’s volume is reduced from 92.7 to 52.2 M. The detection precision and recall have been enhanced by approximately 1.27% and 0.44%, respectively. In terms of real-time detection performance, the rates for lump coal and conveyor belt surface breakage achieved 106.38 and 132.74 FPS, respectively. It is evident that the updated LEC YOLOv5 algorithm is capable of efficiently monitoring and precisely identifying the operational status of the conveyor belt. This innovation offers dependable safety and promotes efficient production within the mining industry.