：As an important part of modern coal mine production, conveyor belts are widely used in the coal collection and transportation. In order to ensure the safe operation of coal mine conveyor belt and solve the drawbacks of the existing conveyor belt longitudinal tear detection technology, a multispectral visual detection method for conveyor belt longitudinal tear is proposed in this paper. The experimental results show that the multispectral visual detection method not only can identify the conveyor belt longitudinal tear, but also accurately classifies and identify other status of the conveyor belt. The accuracy rate of conveyor belt longitudinal tear detection is over 96.5%, and the average accuracy rate of all status of conveyor belt identification is over 96.1%. The proposed method is verified to meet the requirements of reliability and real-time in industrial field.

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As a key equipment for production and transportation, conveyor belts are widely used in the coal mining industry. Once the conveyor belt longitudinal tear occurs, it will seriously affect the production and even cause personal injury. Therefore, the longitudinal tear detection of the conveyor belt is extremely important. In this paper, the sound detection is introduced into the longitudinal tear detection of the conveyor belt, and an audio-visual detection method for conveyor belt longitudinal tear is proposed. Camera and microphone array are used to collect the image and sound signals of conveyor belt, and the conveyor belt is detected from both image and sound, respectively. Then the image and sound analysis results are combined to comprehensively judge the status of the conveyor belt. The experimental results show that the audiovisual detection method can accurately identify the normal, abnormal, and longitudinal tear of the conveyor belt. The detection accuracy is over 86.72% and the sensitivity of longitudinal tear detection is greater than 92.59%. The proposed audio-visual detection method is verified to meet the requirements of longitudinal tear detection of coal mining industry conveyor belts.

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