Adaptive Multi-View Image Mosaic Method for Conveyor Belt Surface Fault Online Detection
Source abstract
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: crossref · Quality: baseline
Research context
OpenAlex topics: Belt Conveyor Systems Engineering · Power Line Inspection Robots · Image and Object Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Tiangong University, Tianjin 300387, China
School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin 300384, China
Tianjin Photoelectric Detection Technology and System Key Laboratory, Tiangong University, Tianjin 300387, China
Funding
National Natural Science Foundation of China · Key Scientific and Technological Support Projects of Tianjin Key R&D Program · Natural Science Foundation of Tianjin City · Tianjin Key Research and Development Program
References (30)
Show source-provided references
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