Instance segmentation is a technique that performs instance-level and pixel-level segmentation of images simultaneously. this paper proposes an instance-aware segmentation network based on a cross-resolution knowledge distillation architecture. The attention mechanism is utilized to fuse high and low-resolution image features for training the Teacher network, which then guides the Student network - taking low-resolution images as input - in feature extraction, ensuring segmentation accuracy while effectively reducing the network's parameter size and computational load. This paper adopts a pruning method based on switchable normalization to prune the backbone of the network, significantly alleviating the computational pressure during the training of the Teacher network, and further enhancing the real-time performance of the Student network. Experimental results on the public COCO dataset and a self-made pedestrian instance segmentation dataset Person-pic show that the proposed instance segmentation network effectively improves real-time segmentation performance while maintaining segmentation accuracy, achieving a balance between precision and speed.

Qiangda Shan, Hao Chen, Ziru Liu. Lightweight Instance Segmentation Algorithm Based on Knowledge Distillation. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 4: 40-48. https://doi.org/10.25236/AJCIS.2025.080405.

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