Improving lightweight model for fabric defect segmentation via contrastive knowledge distillation

Abstract

Fabric defect segmentation is an important part of ensuring the quality of product production. Using fabrics with surface defects will affect the quality and reputation of their products. In previous studies, some model compression methods have helped semantic segmentation models to be deployed on resource-limited working devices. However, the capacity reduction of models usually leads to a decline in detection performance. We propose a knowledge distillation method combining traditional KD loss and contrastive relational distillation, which makes student models learn the differential representation among various defects while receiving knowledge transfer from the teacher model. We use DeepLabV3+ and PSPNet with MobileNetV2 backbone networks as student models to validate our method. Experiment shows that our method performs better than direct training and traditional knowledge distillation methods on the DAGM and AITEX datasets. Our method enables lightweight models to achieve higher performance on fabric defect segmentation tasks.