# Mini-Crack Detection of Conveyor Belt Based on Laser Excited Thermography
## 1 Introduction  
Conveyor belt is one of the critical components of belt conveyor system for continuously bulk materials transportation. It carries and transports various of powder, granular, and bulk materials [1]. With the widely use of belt conveyor system in the industrial field, the equipment safety has been widely concerned [2]. For example, belt fracture or longitudinal tear accidents account for a considerable part of all conveyor belt safety accidents. Hence, the research of the tear detection of conveyor belts is of great significance [3, 4]. A rubber-textile conveyor belt is usually made of three-layer composite materials, in which the rubber is used for the upper and bottom layers, and nylon and steel cord are used for the belt core. The tear of conveyor belt generally occurs when lumpy material first rubs against the upper layer and then penetrates the lower covering layer [5]. The entire tearing process goes through scratch, wear, puncture, and longitudinal tear [6]. In order to prevent the conveyor belt from breaking due to tearing and repair the belt, it is necessary to detect the early damage of the conveyor belt. At present, there are generally two methods of conveyor belt crack detection: contact detection and non-contact detection. The contact detection methods usually add an extramechanical component [7], embed an induction coil [8] or a radio frequency identification device [9] into the conveyor belt for tear detection. However, the contact detection method would gradually reduce the detection accuracy due to contact wear. Therefore, the non-contact defect detection method has been gradually developed. More and more defect detection methods based on machine vision are proposed in non-contact detection methods [10]. In ref. [11], an improved single-scale Retinex (SSR) algorithm is used for image enhancement, and the features--including the area, damage level, and squareness--are extracted by the greyscale image. Reference [12] aims at surface damage identification of conveyor belts. A multi-class support vector machine (SVM) real-time detection system is proposed based on visual saliency. In order to further improve the identification accuracy, [PERSON] et al. [13] propose a longitudinal tear detection of a conveyor belt under uneven light based on Haar-Ada Boost and Cascade algorithm. [PERSON] et al. [14] propose a new detection method that can simultaneously detect multiple faults based on an improved Yolov3 algorithm. Nevertheless, these methods based on visible light images and image processing algorithm are very sensitive to light intensity and dusty environment, which would trigger false alarms. In order to highlight the crack features more obviously, auxiliary equipment is applied to the crack detection of conveyor belt. [PERSON] et al. [15] use laser and area light sources to enhance the contrast between conditional features and conveyor belt surface. They propose a false corner filtration and single-point feature identification method to detect longitudinal tears. In ref. [16], two red laser stripes are projected on the back of the upper conveyor belt to improve detection efficiency and precision. At the same time, many scholars [17] consider the local heating of the conveyor belt caused by the friction between materials and the conveyor belt, and adopted infrared vision to detect conveyor belt tears. Reference [18] proposes the bidirectional reflectance distribution function (BRDF) to build models of infrared spectroscopic imaging. [PERSON] et al. [19] use only one infrared camera to capture images for early-warning of longitudinal tear and proposed a simple automatic threshold select algorithm for the infrared image binarization. [PERSON] et al. [20] propose a novel approach based on infrared spectrum analysis for early warning of longitudinal tearing of the conveyor belt. In ref. [21], an integrative binocular vision detection (IBVD) method is proposed with visible and infrared vision fusion.