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

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.