# A New Knowledge-Distillation-Based Method for Detecting Conveyor Belt Defects
## 1 Introduction  
The mineral transportation conveyor belt is an indispensable piece of equipment in the production and transportation of minerals. During operation, due to the friction and bumping with ore for a long time, which is also affected by factors such as the hardening and aging of the rubber, the surface of the conveyor belt will often appear to have edge loss, surface cracking, holes, covering rubber bulging or skinning, large area wear, deep scratches and other damages. Serious breakage is a precursor to the tearing of the conveyor belt, and if the breakage is not dealt with in time, the breakage will continue to expand with the increase in running time. Finally, destructive tearing can occur, which is a major hazard to the safety of personnel and property [1]. The current traditional manual inspection method requires regular inspection by running the machine at a low speed when the machine is empty, and this inspection method is limited by the time of inspection and the accuracy of the workers' inspection, which makes it difficult to detect damage on the surface of the conveyor belt in a timely, accurate and stable manner. To solve the problems of traditional manual inspection methods and improve the efficiency, time and reliability of conveyor-belt-damage detection, [PERSON] et al. [2] proposed a scheme for the nondestructive inspection of conveyor belts using X-rays. [PERSON] et al. [3] proposed a method for detecting longitudinal tears in conveyor belts using infrared images, which performs ROI(region of interest) selection and the binarization of the images, and then determines whether an early warning should be issued based on the number of connected. [PERSON] et al. [4] proposed an early warning method for longitudinal tear detection in conveyor belts based on infrared spectral analysis, where the spectral characteristics of the infrared radiation field are used to determine whether there is a risk of longitudinal tear on the conveyor belt. [PERSON] et al. [5] proposed a longitudinal tear detection methodbased on visible charge-coupled devices (CCDs) and infrared CCDs. Combining two CCDs yields a more reliable conveyor belt tear detection method. In recent years, with the continuous development of deep-learning technology and its great progress in the field of computer vision, target detection models based on deep-learning technology have also achieved better results in conveyor-belt-damage detection tasks. Unlike traditional computer vision methods that focus on processing images using fixed algorithms and processes to extract specific regions of the image, deep-learning-based computer vision techniques can achieve a more accurate, faster, and more intelligent detection by extracting deep image features of the target from a large number of data samples. At present, deep-learning-based target detection algorithms are mainly divided into two-stage detection algorithms represented by Mask R-CNN [6] and Faster R-CNN [7], and single-stage detection algorithms represented by SSD [8], RetinaNet [9] and YOLO [10]. The former is slower because it first generates the candidate frames of the target region, and then classifies each candidate frame, requiring two steps to complete the detection of the target. The latter can predict all the bounding boxes by feeding the images into the network only once, which makes it faster and is more often used in industrial scenarios. As shown in Table 1, a number of studies have been conducted on conveyor belt detection using deep-learning-based methods. [PERSON] et al. [11] proposed a conveyor-belt-detection method using EfficientNet to replace YOLOv3's backbone network Darknet53, which achieved 97.26% detection accuracy. [PERSON] et al. [12] proposed a detection model combining BTFPN and YOLOX to achieve 98.45% detection accuracy for conveyor belt damage. To improve the speed of detection of conveyor belt damage, [PERSON] et al.