Abstract:  In order to solve the problem of unstable lighting conditions, dust and other interference factors when the camera monitors the distribution of mineral materials on the conveyor belt of the coal mine, the effect of directly applying binarization to the camera image to obtain the distribution of mineral materials is unstable and prone to missed inspections, a conveyor belt spill detection technology based on Cycle GAN (Cycle Generative Adversarial Networks) image enhancement is proposed. First, the image of the coal mine conveyor belt collected by the camera is used as input, and the image is enhanced through Cycle GAN; after that, the binary method is used to segment the image to accurately obtain the target area of the conveyor belt; finally, the threshold method and morphological processing are used to analyze the conveyor belt. The belt spraying area is judged and detected. The experimental results show that this technology can effectively monitor the spillage on the conveyor belt, and can improve the monitoring accuracy on the basis of traditional monitoring methods.

Key words: cycle generative adversarial networks(Cycle GAN), image enhancement, conveyor belt, detection