TsDa-ASAM: Balancing efficiency and accuracy in coke image particle size segmentation via two-stage distillation-aware adaptive segment anything model

Abstract

Coke image segmentation is a crucial step in coke particle size control of the sintering process. However, due to the complexity of model architecture and the dense distribution of coke particles in the images, existing segmentation methods fail to satisfy the efficiency and accuracy requirements for coke image segmentation in industrial scenarios. To address these challenges, this paper proposes a two-stage distillation-aware adaptive segment anything model to balance efficiency and accuracy in coke image particle size segmentation, referred to as TsDa-ASAM. In the first stage, knowledge distillation methods are employed to distill the Segment Anything Model (SAM) into a lightweight model, explicitly focusing on enhancing segmentation efficiency. In the second stage, a domain knowledge injection strategy is formulated, which incorporates domain knowledge into the distillation model to effectively enhance the accuracy. Moreover, an adaptive prompt point selection algorithm is introduced to address the redundancy issue of prompt points in SAM, improving the efficiency of TsDa-ASAM. The effectiveness of TsDa-ASAM is validated through extensive experiments on the publicly available dataset SA-1B and the coke image dataset from industrial sites. After distillation and fine-tuning, the segmentation accuracy of the proposed model improved by 10%, and the segmentation efficiency of TsDa-ASAM was enhanced by 2 to 3 times with the integration of the adaptive prompt point selection algorithm. The experimental results have effectively demonstrated the potential of the proposed model in balancing accuracy and efficiency.