Xiaoli Hao1,*, Xiaojuan Meng1, Yueqin Zhang1, JinDong Xue2, Jinyue Xia3
CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2671-2685, 2021, DOI:10.32604/cmc.2021.016856
Abstract In underground mining, the belt is a critical component, as its state directly affects the safe and stable operation of the conveyor. Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations. This tends to cause a large amount of calculation and low detection precision. To solve these problems, in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network (CDCGAN) was designed. In the traditional DCGAN, the image generated by the generator has a… More >
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