Optimization of online detection algorithm for foreign matters on belt conveyor in underground coal mine
In order to solve the problems in the foreign matter detection on belt conveyor of underground coal mine caused by the low accuracy of traditional detection algorithms under the interference of coal dust,uneven light,and high-speed movement of the conveyor belt,the detection algorithm of mine belt foreign objects was optimized based on YOLOv7.Firstly,the adaptive contrast enhancement algorithm was used to strengthen the contrast of the belt monitoring image and improve the clarity of the target image contour.Secondly,a multi-scale mixed residual attention mechanism was proposed in the backbone extraction network to enhance YOLOv7's ability to extract foreign body features and interfere with background.Finally,the weighted bidirectional feature pyramid network and 4 detection heads were used to output the model prediction results to improve the efficiency of foreign object detection of different sizes.According to the experiment results,the improved YOLOv7 model was superior to YOLOv5 and YOLOv7 in the recognition accuracy and speed of foreign objects in the underground belt,and the recognition accuracy and speed of foreign objects in the underground belt were 93.6%and 26 f/s,respectively.Compared with YOLOv5 and YOLOv7,the average recognition accuracy rate of the proposed model was increased by 3.9%and 3.1%,respectively;the average recall rate was increased by 4.1%and 3.4%,respectively;the detection time was improved by 0.009 s and 0.005 s respectively.
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