# Adaptive Multi-View Image Mosaic Method for Conveyor Belt Surface Fault Online Detection
## 1 Introduction  
Belt conveyor is a kind of continuous transportation equipment in modern production that has been widely used in the coal, mining, port, electric power, metallurgy, and chemical industries, as well as in other fields [1]. The conveyor belt is the key component of the belt conveyor for traction and load-bearing, which can easily produce surface scratch, longitudinal tear, or fracture [2]. If these faults are not detected and handled in time, they may cause equipment damage, material loss, and other substantial economic losses, and even cause personal casualties, affecting safety production [3]. At present, machine vision technology can be used for the online detection of conveyor belt surface fault [3; 4; 5; 6; 7]. However, due to the structural characteristics of the conveyor, the distance between the upper conveyor belt and the lower conveyor belt is small. The sight distance is small, and the bandwidth and field of view are large. Single-view conveyor belt surface fault detection is difficult to meet the bandwidth requirements, and there are blind areas and unsatisfactory imaging effects. Therefore, multi-view detection is needed, and multi-view conveyor belt images need to be stitched online. At present, there are three main image mosaic methods: the method based on gray information, the method based on frequency domain correlation, and the method based on feature correlation. Among them, the method based on feature correlation has been widely focused upon because of its good robustness. The more mature feature operators are SIFT (scale-invariant feature transform), SURF (speeded up robust features), BRIEF (binary robust independent elementary features), ORB (oriented FAST and rotated BRIEF),etc. SIFT algorithm was proposed by [PERSON] in 1999 and improved in 2004. It not only has good robustness in image rotation and scale scaling, but also can maintain good stability against noise interference, brightness, and viewing angle changes [8; 9]. However, the algorithm does not consider the distribution of feature points. It will extract too many feature points in the region with intricate details, which can easily produce false matching. The algorithm is complex in its calculation and is time-consuming. Thus, it is difficult to meet real-time performance. For this reason, [PERSON] et al. [10] proposed an image mosaic algorithm based on the combination of region segmentation and SIFT, which improved the speed of the image. However, the amount of calculation is still large. [PERSON] et al. [11] proposed SURF to overcome the shortcomings of the SIFT algorithm. The SURF algorithm inherits the scale and rotation invariance of the SIFT algorithm and adopts the Haar feature and the integral image concept, making the calculation more efficient. The speed is about three times faster than SIFT. However, the SURF algorithm also has the problem of wrong matching, which reduces the accuracy of the matching results and dramatically slows down the speed due to unstable feature points and wrong matching. Therefore, there are many studies on the performance improvement of the SURF algorithm. [PERSON] et al. [12] constructed a k-dimensional (K-D) tree and improved the best bin first (BBF) algorithm instead of the linear algorithm to speed up SURF matching. [PERSON] et al. [13] proposed an improved SURF algorithm with a good application prospect in low light conditions. [PERSON] et al. [14] used Haar wavelet response to establish a descriptor for each feature point and calculated the normalized gray difference and two-step degree of the neighborhood to form a new feature descriptor. The SURF algorithm was improved to make it robust and stable to image blur, illumination difference, angle rotation, and field of view transformation. From overall technology development, image stitching technology has become more and more mature and stable. There are many excellent image stitching algorithms [15; 16; 17; 18; 19; 20; 21; 22] that have achieved good results in some scenes.