An automatic coke optical texture recognition method based on semantic segmentation model

Article An automatic coke optical texture recognition method based on semantic segmentation model Xialin Wang 1, Xiu Kan 1,*, Zhen Zhang 1, and Weizhou Sun 2 1 Shanghai University of Engineering Science, Shanghai 201620, China 2 Anhui University of Technology, Anhui 243002, China * Correspondence: [email protected] Received: 1 October 2023 Accepted: 30 October 2024 Published: 24 December 2024 Abstract: To solve the segmentation problem of coke optical texture in coke photomicrograph, a semantic segmentation method is proposed based on the multi-scale feature fusion and attention strategy in this paper. The multi-scale module is com­bined with convolutional block attention module (CBAM) to design a feature extraction strategy, and the Coke-Net network model is established to extract the coke optical texture from coke photomicrographs. The relationship between pixels is fully considered to refine the segmentation edge, and the extraction results with spatial consistency are output to complete the precise segmentation of the coke optical structure. The ablation experiment and contrast experiment are used to demonstrate the effectiveness of the proposed method in coke optical texture extraction.