The impact of a number of samples on unsupervised feature extraction, based on deep learning for detection defects in printed circuit boards
Deep learning provides new ways for defect detection in automatic optical inspections (AOI). However, the existing deep learning methods require thousands of images of defects to be used for training the algorithms. It limits the usability of these approaches in manufacturing, due to lack of images of defects before the actual manufacturing starts. In contrast, we propose to train a defect detection unsupervised deep learning model, using a much smaller number of images with-out defects. We propose an unsupervised deep learning model, based on transfer learning, that ex-tracts typical semantic patterns from defectâfree samples (oneâclass training). The model is built upon a preâtrained VGG16 model. It is further trained on custom datasets with different sizes of possible defects (printed circuit boards and soldered joints) using only small number of normal samples. We have found that the defect detection can be performed very well on a smooth back-ground; however, in cases where the defect manifests as a change of texture, the detection can be less accurate. The proposed study uses deep learning selfâsupervised approach to identify if the sample under analysis contains any deviations (with types not defined in advance) from normal design. The method would improve the robustness of the AOI process to detect defects.