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    Conveyor Belt Detection Based on Deep Convolution GANs

    Xiaoli Hao1,*, Xiaojuan Meng1, Yueqin Zhang1, Jindong Xue2, Jinyue Xia3

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 601-613, 2021, DOI:10.32604/iasc.2021.017963

    Abstract The belt conveyor is essential in coal mine underground transportation. The belt properties directly affect the safety of the conveyor. It is essential to monitor that the belt works well. Traditional non-contact detection methods are usually time-consuming, and they only identify a single instance of damage. In this paper, a new belt-tear detection method is developed, characterized by two time-scale update rules for a multi-class deep convolution generative adversarial network. To use this method, only a small amount of image data needs to be labeled, and batch normalization in the generator must be removed to… More >

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