A fast gangue detection algorithm based on multi-head self-attention mechanism and anchor frame optimization strategy

Abstract

ABSTRACT Because of the problem of difficult identification, model acceleration, and localization for coal gangue detection, a fast coal gangue detection algorithm based on a multi-head self-attention mechanism and anchor frame optimization strategy is designed. Firstly, the multi-head self-attention mechanism MHSA is combined with the CNN backbone structure of YOLOv5s, which is used to enhance the algorithm’s ability to perceive global information to strengthen the model’s recognition of coal and gangue; Secondly, the lightweight Ghost module will be used to reconfigure the backbone of YOLOv5s and the feature fusion layer to reduce the complexity of the model and increase the speed of the coal gangue image detection; And lastly, the selection of an appropriate strategy of input image scale and anchor frame size to improve the performance of the detection algorithm to solve the problem of localization of coal and gangue. Experiments show that the average accuracy of the improved MYL model mAP_0.5 reaches 91.8%, the detection accuracy of mAP_0.5 is improved by 3.6%, and the detection speed is 80 FPS. The improved MYL model can show stronger robustness and superior performance in the detection of coal gangue and can meet the requirements of effective industrial detection.