关键词: 皮带机, 双时间尺度更新规则, 多分类, 深度卷积生成对抗网络, 皮带撕裂检测

Abstract:

Belt tearing is one of the most common faults of belt conveyor, which directly affects the safe and stable operation of belt conveyor. In view of the fact that most of the existing methods only detect one type of damage, a belt tear detection method based on two time-scale update rule and multi-class deep convolutional generative adversarial network is designed. The image of the belt surface is captured by CCD camera and transmitted to the decision subsystem by the data transmission subsystem. In the processing module of the decision subsystem, by removing the batch normalization operation of the generator, the damage location and type can be quickly obtained by multi-class deep convolutional generative adversarial network. The introduction of two time-scale update rule makes the model converge faster. The experimental results show that the mean average precision on the MS COCO data set is 95.7%, the mean average precision is 96.9% on the belt image data set.

Key words: belt conveyor, two time-scale update rule, multi-class, deep convolutional generative adversarial networks, belt tear detection