Intelligent identification method of foreign objects on coal mine belt conveyor based on RetinaNet deep network
In order to solve the scale change problem of foreign objects identification and improve the safety and efficiency of coal mine production,an intelligent identification method for foreign objects on coal mine belt conveyor based on the RetinaNet deep network was proposed.Local gamma transform and single parameter homomorphic filter were used to enhance the coal mine belt conveyor image,which provided a good basis for the subsequent foreign objects identification.Inputting the enhanced images into the RetinaNet deep network,the depth features of coal mine belt conveyor images were extracted through its internal feature pyramid network,and border regression and classification were achieved through inputting them into each subnet,and loss functions were defined to adjust the network.The RetinaNet deep network was trained through a large number of images containing foreign objects on the belt conveyor,and intelligent identification of foreign objects on the belt conveyor was finally realized through the trained network.The experimental verified that the method could quickly and accurately identify foreign bodies,and accurately mark foreign bodies through an eye-catching red label box,improve the continuity and stability of belt conveyor transportation,and effectively improve the production efficiency and economic benefits of coal mines.
RetinaNet deep networkcoal mine belt conveyor transportationintelligent identification of foreign bodieslocal gamma transformsingle parameter homomorphic filterfeature pyramid network
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