The existing coal quantity detection method of belt conveyor is affected by the undergrounddark environment resulting in a comparatively lower recognition accuracy and a new one suitable forunderground environment is proposed. Based on the fact that the acquisition of depth image is not af-fected by the dark underground environment, the depth images of different coal amounts obtained by thedepth camera are taken as the research object, and the filtering processing is carried out to filter out theinterference information and enhance the feature information. A DID-CNN recognition network is deter-mined to extract the features of the filtered coal depth image, and the coal amount finally is divided into第 5 期刘 飞 , 等 : 基 于 深 度 图 像 的 带 式 输 送 机 煤 量 检 测 方 法three different categories as the detection results, which can be used for the grading control of the beltspeed of the belt conveyor. The results show that the accuracy of the proposed coal quantity detectionmodel is 99. 3% , the F1 score of the model is 0. 991, and the average detection time of each image is0. 024 3 seconds. The research indicate that the coal quantity detection method of belt conveyor basedon depth image can effectively eliminate the interference caused by the underground dark environmenton the coal quantity detection, with detection accuracy higher and processing faster. This method canprovide support for improving the transportation efficiency of belt conveyor, realizing energy saving andconsumption reduction, and prolonging the service life of equipment.
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