Coal gangue image segmentation method based on edge detection theory of star algorithm

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.

KEYWORDS:

• Coal
• coal gangue
• star algorithm
• edge detection
• image segmentation

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant (No.51904007, 51874004), in part by the Anhui Province Science and Technology Major Special Funding Project under Grant (No.18030901049), in part by General Project of China Postdoctoral Science Foundation (No. 2019M662133), in part by the Anhui Natural Science Foundation Project under Grant (No.1908085QE227), in part by the Key Research and Development Program of Anhui Province under Grant (No.202004a07020043), and in part by the Anhui University of Science and Technology College Student Venture Fund (No.2021KJCX03).