Research on Detection of Large Coal Blockage at the Transfer Point of Belt Conveyor Based on Improved Mask R-CNN

Abstract Aiming at the problem of coal accumulation and blockage at the transfer point at the transfer point due to the presence of large coal in the coal belt conveyor, a method based on the improved Mask R-CNN for the detection of the jam at the transfer point of the belt conveyor is proposed. The method firstly adds the SENET and SKNet models to the Mask R-CNN feature extraction part ResNet50, enhances the function of extracting features, and cancels the segmentation part of Mask R-CNN. In order to make the network better converge, the loss function in the RPN network is optimized, and the original IOU is replaced with GIOU, which solves the problem that the IOU cannot be optimized when it is 0 and the IOU cannot distinguish the anchor from the ground truth.