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

Coal is an essential national energy source, which is mainly conveyed by the conveyor belts in the process of coal production and transportation. However, when transferred by means of the conveyor belt, lump of coal affects the safe operation of the conveyor belt. For the problem of lump coal monitoring during coal transportation, so the AMGC YOLOv5（AHE Mosaic Ghost CBAM YOLOv5） lightweight neural network based method for the real time monitoring of lump coal on conveyor belt is proposed. First, Image data pre-processing using adaptive histogram equalization (AHE), reducing the impact of coal dust, dust and underground coal mine light on target monitoring, and to enhance the clarity and contrast of the data set. At the same time combined with Mosaic data to increase the richness of the data set. Subsequently, The Ghost Net lightweight neural network is introduced to make full use of the relationship between feature extraction and redundancy characteristics of feature maps, and combine traditional convolutional operation with lightweight linear operation, which greatly reduces the number of parameters of the model and the amount of floating point computation while ensuring the detection precision. Eventually, combined with Convolutional Block Attention Module (CBAM) mechanism to complete the channel feature extraction and spatial feature extraction sequentially. And to improvement the tendency of feature extraction, improve the characterization ability reduce the interference of background images. The experimental results showed that: The improved AMGC YOLOv5 algorithm in terms of model lightweight, The number of network layers is reduced by 145, and the model volume is compressed by 58.8 M.The number of parameters and floating point calculations are cut by about 63.60% and 68.86%.Training hours were reduced by about 0.76 hours , and GPU usage was reduced by about 21.78%.In addition, in terms of model performance, Precision was improved by about 0.91%, and real-time monitoring efficiency was improved by about 38.05% from 68.34 FPS to 94.34 FPS. It can be seen that AMGC YOLOv5 not only realizes lightweight, but also can effectively improve the model's various detection performance.