![]() ![]() With the development of industrial automation, conveyor belts have been used as one of the most significant pieces of equipment for the transportation of various industrial products. This paper provides professional guidelines and promising research directions for researchers and engineers based on the leading theories in machine vision and deep learning. Lastly, a case study was carried out to compare the detection performance of popular techniques using industrial image datasets. Moreover, the current challenges and opportunities for conveyor belt defect detection are discussed. Furthermore, image dataset preparation and data imbalance problems are studied for belt defect detection. Then, recent mainstream techniques and theories for conveyor belt tear detection are reviewed, and their characteristics, advantages and shortcomings are discussed. ![]() Next, the causes of conveyor belt tear are classified, such as belt aging, scratches by sharp objects, abnormal load or a combination of the above reasons. First, the basic structure of conveyor belts is discussed and an overview of tear defect detection methods for conveyor belts is studied. In this paper, we discuss existing techniques used in industrial production and state-of-the-art theories for conveyor belt tear detection. Inspection and defect detection is essential for conveyor belts, both in academic research and industrial applications. As the core part of the conveyor, the belt is vulnerable to various failures, such as scratches, cracks, wear and tear. ![]() ![]() The belt conveyor is the most commonly used conveying equipment in the coal mining industry. ![]()
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