Abstract
In order to improve the accuracy and real-time of image mosaic, realize the multi-view conveyor belt surface fault online detection, and solve the problem of longitudinal tear of conveyor belt, we in this paper propose an adaptive multi-view image mosaic (AMIM) method based on the combination of grayscale and feature. Firstly, the overlapping region of two adjacent images is preliminarily estimated by establishing the overlapping region estimation model, and then the grayscale-based method is used to register the overlapping region. Secondly, the image of interest (IOI) detection algorithm is used to divide the IOI and the non-IOI. Thirdly, only for the IOI, the feature-based partition and block registration method is used to register the images more accurately, the overlapping region is adaptively segmented, the speeded up robust features (SURF) algorithm is used to extract the feature points, and the random sample consensus (RANSAC) algorithm is used to achieve accurate registration. Finally, the improved weighted smoothing algorithm is used to fuse the two adjacent images. The experimental results showed that the registration rate reached 97.67%, and the average time of stitching was less than 500 ms. This method is accurate and fast, and is suitable for conveyor belt surface fault online detection.
Highlights
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]
Machine vision technology can be used for the online detection of conveyor belt surface fault [3,4,5,6,7]
This paper presents an adaptive multi-view image mosaic method for conveyor belt surface fault online detection based on grayscale and feature combination
Summary
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 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]. 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. Multi-view detection is needed, and multi-view conveyor belt images need to be stitched online
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