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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 avoid artifacts in the generated images. The output of the discriminator uses a multi-classification softmax function to identify the scratches, cracks, and tears in the belt. In addition, we have improved the two time-scale update rule, by which the generator and discriminator use different learning rates, updated it according toα  :  β, and definedα  :  βas 2:1. It can strike a balance between the generator and discriminator, speed up discriminator training, and improve real-time damage detection. Experimental results show that the detection accuracy of tears reaches 100%, and the detection accuracy of non-serious damage is up to 97.1%.

Conveyor Belt Detection Based on Deep Convolution GANs