Xiaoli Hao^1,*, 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 avoid artifacts in the generated… More >

Xiaoli Hao^1,*, 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 >