Edge detection of low illumination image in cutting unit of shearer

In this paper, a fractional differentiation-based edge detection algorithm named Lif is proposed to address the edge detection problems of missing edge and fuzzy details in the cutting unit of shearers working in low-light underground environments. First, the initial fractional mask operator was built using a larger detection template according to the Grünwald-Letnikov definition of the fractional derivative. Then, the weight coefficients at various positions of the mask operator were determined according to the theory of Pascal’s triangle, and the mask operator was extended to four different directions. Finally, the mask operator was convolved with the image, and the local feature information of the image was used to process the differentiation results in all directions. The results show that (1) the Lif algorithm can obtain the edge information in different directions in the image more comprehensively when conducting experiments on low-light images in different scenarios, has stronger noise resistance when processing low-light images, and can retain more textural details; the edge lines extracted by this algorithm are clearer and more complete than those extracted by other edge detection algorithms. (2) Compared with the edge detection algorithm based on the fractional grey system model and the improved fractional Sobel edge detection algorithm, the Lif algorithm performs better than them by 43% and 11% in terms of Entropy, by 23% and 23% in terms of AG, and by 152% and 6% in terms of SSIM, indicating that the Lif algorithm has advantages when detecting the edge for cutting unit of shearers. This study is of great significance for improving the operational safety and reliability of underground equipment such as shearers.