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

文献类型：期刊论文

作者Zheng, Ye1,2; Cui, Li1
刊名APPLIED INTELLIGENCE
出版日期2022-06-11
页码16
ISSN号0924-669X
关键词Defect detection Unseen samples Siamese attention Convolutional neural network Template image
DOI10.1007/s10489-022-03595-0
英文摘要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.
资助项目National Natural Science Foundation of China (NSFC)[61672498]
WOS研究方向Computer Science
语种英语
出版者SPRINGER
WOS记录号WOS:000809541300004
源URL[http://119.78.100.204/handle/2XEOYT63/19624]  
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.

入库方式： OAI收割

来源：计算技术研究所

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