Adaptive image enhancement and particle size identification method based on coal and gangue

Abstract

Abstract To reduce the influence of material particle size on coal gangue identification, a particle size identification method, and an adaptive image enhancement method are proposed, which can accurately identify the particle size of poorly segmented and mutually blocked materials, effectively reduce the reflection and blur of the image surface and enhance the texture details. Through the research of coal gangue images with different particle sizes, it is found that the image quality and feature curve distribution of small particle size are different from those of large particle size, and the gradient features are worse. In this paper, the accurate identification of particle size is realized using the difference in image quality and texture, and the identification rate is 99.25%. Through the image enhancement method in this paper, 33.41% of the reflection on the image surface is removed, and the average gradient is improved by 74.01%, which effectively improves the image quality and the ability to express texture information. This algorithm has high environmental adaptability, and the identification rate can reach 99.16% in moderate illumination, 98.33% in dim illumination, and 96.33% in strong illumination. This research provides a valuable idea for image processing and identification technology based on machine vision.