Damage Detection Method of Mining Conveyor Belt Based on Deep Learning
Ming Liu, Qigang Zhu, Yanfang Yin, Yong Fan, Zihan Su, Shuaishuai Zhang
Abstract
The belt conveyor is one of the key equipment in coal mining. Timely identification of belt damage is necessary to ensure coal mine safety production and reduce economic losses. In order to meet the needs of practical application, a new method of belt damage identification is proposed by fusing the temporal and spatial features of the image and directly using the on-site monitoring video image. Based on the deep learning network structure, an end-to-end belt damage detection model is designed. An improved attention mechanism is designed and introduced into the image spatial feature extraction model to solve the problem of complex background in belt surveillance video. In order to reduce the influence of reflection and shadow on recognition accuracy, a method of extracting temporal features of continuous multi frame video images using Temporal Convolutional Networks (TCN) is designed. The experimental results show that the accuracy of the method for belt damage recognition by fusing the temporal and spatial features of the image is more than 20% higher than that of the method for extracting the spatial features of the image alone, which shows that our proposed method is suitable for belt damage detection directly using the on-site belt monitoring video and is practical.