Defect detection on new samples with siamese defect-aware attention network

doi:10.1007/s10489-022-03595-0

	Defect detection on new samples with siamese defect-aware attention network
	Zheng, Ye 1,2; Cui, Li 1
	2022-06-11
发表期刊	APPLIED INTELLIGENCE
ISSN	0924-669X
页码	16
摘要	Deep learning-based methods have recently shown great promise in the defect detection task. However, current methods rely on large-scale annotated data and are unable to adapt a trained deep learning model to new samples that were not observed during training. To address this issue, we propose a new siamese defect-aware attention network (SDANet) with a template comparison detection strategy that improves the defect detection technique for matching new samples without rapidly collecting new data and retraining the model. In SDANet, the siamese feature pyramid network is used to extract multi-scale features from input and template images, the defect-aware attention module is proposed to obtain inconsistency between input and template features and use it to enhance abnormality in input image features, and the self-calibration module is developed to calibrate the alignment error between the input and template features. SDANet can be used as a plug-in module to enable most existing mainstream detection algorithms to detect defects using not only the features of defects, but also the inconsistency between features of the inspected image and the template image. Extensive experiments on two publicly available industrial defect detection benchmarks highlight the effectiveness of our method. SDANet can be seamlessly integrated into mainstream detection methods and improve the mAP of mainstream detection algorithms on unseen samples by 12% on average which outperforms current state-of-the-art method by 7.7%. It can also improve the performance in seen samples by 4.3% on average. SDANet can be used in general defect detection applications of industrial manufacturing.
关键词	Defect detection Unseen samples Siamese attention Convolutional neural network Template image
DOI	10.1007/s10489-022-03595-0
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China (NSFC)[61672498]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000809541300004
出版者	SPRINGER
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19625
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Cui, Li
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zheng, Ye,Cui, Li. Defect detection on new samples with siamese defect-aware attention network[J]. APPLIED INTELLIGENCE,2022:16.
APA	Zheng, Ye,&Cui, Li.(2022).Defect detection on new samples with siamese defect-aware attention network.APPLIED INTELLIGENCE,16.
MLA	Zheng, Ye,et al."Defect detection on new samples with siamese defect-aware attention network".APPLIED INTELLIGENCE (2022):16.