Coal-rock interface perception and accurate recognition in heading face under coal dust environment based on machine vision

The coal-rock identification technology in the roadway excavation process is the core of the automatic adjustment of roadheader’s cutting head, and it is also one of the key problems restricting the development of intelligent mines. In view of the current mining imbalance, the excavation face lacks a mature and effective coal-rock identification scheme, and the existing image based coal-rock identification models have problems such as poor segmentation accuracy and inability to flexibly deploy, a coal-rock cutting interface perception and precise recognition method based on image segmentation is proposed in the heading face. This method combines the actual cutting situation of the excavation working face and uses the MobileNetV2 feature extraction network as the backbone network of DeepLabV3+, so that the model can better balance the segmentation accuracy and model complexity. The channel attention (SE) operation is performed on the advanced features output by the Atrous Spatial Pyramid Pooling module, and ch