Year: 2021
Type: article
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.
Source: Applied Sciences
DOI: 10.3390/app11062564
Authors: Rui Gao, Changyun Miao, Xianguo Li
Institutions: Tianjin Chengjian University, Tiangong University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,160
Awards: G4439842690, G6023183808
OpenAlex ID: W3136534844