  Surface defect detection is one of the most essential processes for
industrial quality inspection. Deep learning-based surface defect detection
methods have shown great potential. However, the well-performed models usually
require large training data and can only detect defects that appeared in the
training stage. When facing incremental few-shot data, defect detection models
inevitably suffer from catastrophic forgetting and misclassification problem.
To solve these problems, this paper proposes a new knowledge distillation
network, called Dual Knowledge Align Network (DKAN). The proposed DKAN method
follows a pretraining-finetuning transfer learning paradigm and a knowledge
distillation framework is designed for fine-tuning. Specifically, an
Incremental RCNN is proposed to achieve decoupled stable feature representation
of different categories. Under this framework, a Feature Knowledge Align (FKA)
loss is designed between class-agnostic feature maps to deal with catastrophic
forgetting problems, and a Logit Knowledge Align (LKA) loss is deployed between
logit distributions to tackle misclassification problems. Experiments have been
conducted on the incremental Few-shot NEU-DET dataset and results show that
DKAN outperforms other methods on various few-shot scenes, up to 6.65% on the
mean Average Precision metric, which proves the effectiveness of the proposed
method.
