Coal gangue image segmentation method based on edge detection theory of star algorithm. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Wang, Xinquan; Wang, Shuang; Guo, Yongcun; Hu, Kun; Wang, Wenshan
• Is Part Of: International journal of coal preparation and utilization. Volume 43:Issue 1(2023)
• Page Start: 119
• Page End: 134
• Publication Date: 2023-01-02
• Subjects: Coal -- coal gangue -- star algorithm -- edge detection -- image segmentation; Coal preparation -- Periodicals; Coal -- Research -- Periodicals; 662.62
• DOI: 10.1080/19392699.2021.2024173
• Languages: English
• ISSNs: 1939-2699

Abstract

ABSTRACT: Aiming at the difficult problem of coal gangue image segmentation in complex backgrounds, this paper proposes an image segmentation method based on the edge detection theory of the star algorithm. The pixel matrix is extracted one by one in the X and Y directions of the coal gangue image, and the central pixel of the matrix satisfying the monotonic change condition is assigned as 0. They are mapped to single-value images with equal size in turn, to realize the detection of coal and gangue edges in the images. The response strategy of adjusting matrix length n and assignment factor β in real-time by using the feedback result of the illuminance meter in changing illumination environment is given. Combining the edge detection method of the star algorithm with the morphological method, the fine segmentation of the coal gangue image is completed. The segmentation results are based on the segmentation results obtained by the AI algorithm, and the error rates of the pixel area and centroid coordinates of coal gangue are within 0.29%. This study provides a novel, precise and efficient solution to the problem of image edge detection and segmentation in complex backgrounds. HIGHLIGHTS: A new method of coal gangue edge detection. Adapt to the complex background and illumination change conditions. The area and position information of coal gangue can be extracted quickly and accurately. It provides a new scheme to solve the background segmentation problem in coal gangue recognition.