遗忘
渐进式学习
计算机科学
特征(语言学)
人工智能
余弦相似度
班级(哲学)
目标检测
机器学习
模式识别(心理学)
数据挖掘
哲学
语言学
作者
Chen Sun,Liang Gao,Xinyu Li,Pai Zheng,Yiping Gao
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:73: 1-11
标识
DOI:10.1109/tim.2023.3343768
摘要
Defect detection is one of the most essential processes for industrial quality inspection. However, in Continuous Defect Detection (CDD), where defect categories and samples continually increase, the challenge of incremental few-shot defect detection remains unexplored. Current defect detection models fail to generalize to novel categories and suffer from catastrophic forgetting. To address these problems, this paper proposes an Incremental Knowledge Learning Framework (IKLF) for continuous defect detection. The proposed framework follows the pretrain-finetuning paradigm. To realize end-to-end fine-tuning for novel categories, an Incremental RCNN module is proposed to calculate cosine-similarity features of defects and decouple class-wise representations. What’s more, two incremental knowledge align losses are proposed to deal with catastrophic problems. The Feature Knowledge Align (FKA) loss is designed for class-agnostic feature maps, while the Logit Knowledge Align (LKA) loss is proposed for class-specific output logits. The combination of two align losses mitigates the catastrophic forgetting problem effectively. Experiments have been conducted on two real-world industrial inspection datasets (NEU-DET and DeepPCB). Results show that IKLF outperforms other methods on various incremental few-shot scenes, which proves the effectiveness of the proposed method.
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