# Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt
## 1 Introduction  
Belt conveyor is one of the most important transportation equipment in the field of bulk material transportation, widely used in coal mines, docks, ports, chemical industries, and other fields. At present, it is developing towards long-distance, high-speed, small-radius space turning, and intelligence [1]. The intelligentization of belt conveyors refers to the realization of self-perception and status adjustment of its operating status through modern sensing technology and artificial intelligence, while the realization of autonomous operation and unattended operation of the equipment [2, 3]. The intelligent transportation system is a safe, efficient, intelligent, and unmanned transportation system that integrates advanced technologies such as intelligent driving, intelligent control, intelligent operation and maintenance, and unmanned driving. Its core lies in intelligent mining transportation equipments [4]. The current research work on the intelligent development of belt conveyors is focused on: energy-efficient equipment or energy-saving technology for belt conveyors, especially load-based energy-saving speed regulation systems for belt conveyors [5, 6, 7, 8, 9, 10, 11]; expert-based fault diagnosis systems based on noise and vibration monitoring [12, 13]; running state detection technology based on vision and image processing: including deviation monitoring [14], belt speed monitoring [15], material flow detection [16], foreign body identification [17], tear detection, roller temperature monitoring [18], etc. This article focuses on the visual monitoring of mining conveyor belt damage. Conveyor belt, as an important component of belt conveyor, plays an important role in carrying materials and traction, with its cost accounts for 30-50% of the total price of belt conveyor [19]. The health of the conveyor belt seriously affects the normal operation of the belt conveyor, which in turn affects the safe and efficient production of the entire enterprise. In the actual production process, conveyor belts often suffer problems such as the falling material impact, chute jamming, piercing by a foreign body, etc., which can easily cause abnormal damage to the conveyor belt, thereby shortening the service life of the conveyor belt and increasing production costs. Further, if the damage of the conveyor belt cannot be detected and treated in time, it may eventually cause the occurrence of the conveyor belt tearing accident, which will cause greater harm. The current detection methods for conveyor belt tearing include: weak magnetic test [20], built-in sensor chip method [21], machine vision method [22; 23; 24; 25], line laser-assisted method [26], infrared camera assisted method [27; 28; 29; 30], audio-visual fusion method [31].Among them, most of the research work only focuses on the detection of tearing of conveyor belt, without the detection of other types of damage, which has a certain limitation. As for the detection of multiple forms of conveyor belt damage, relatively little research work has been done on the subject. A conveyor belt damage detection method based on ADCN (Adaptive Deep Convolutional Network), which is essentially a variant of SPP-Yolov3, was mentioned in Ref. [32], enabling the detection of both Scratch and Tearing damage states. Then a deep learning-based detection method was mentioned in Ref. [3]: by constructing a conveyor belt damage dataset and classifying the belt damage types into four categories, namely surface wear, surface damage, breakdown, and tear, an Efficientnet-Yolov3-based target detection network was proposed to classify and locate the damage, achieved the highest prediction accuracy of 97.26% and the fastest prediction speed effect of 42 FPS on the dataset. The work of this paper mainly focuses on improving the detection speed of conveyor belt damage based on deep learning method, which is to be realized through the lightweight of target detection network.