| Summary: | Detecting defects is an integral part of any manufacturing process. Most works still utilize traditional image processing algorithms to detect defects owing to the complexity and variety of products and manufacturing environments. In this paper, we propose an approach based on deep learning which uses autoencoders for extraction of discriminative features. It can detect different defects without using any defect samples during training. This method, where samples of only one class (i.e. defect-free samples) are available for training, is called One Class Classification (OCC). This OCC method can also be used for training a neural network when only one golden sample is available by generating many copies of the reference image by data augmentation. The trained model is then able to generate a descriptor—a unique feature vector of an input image. A test image captured by an Automatic Optical Inspection (AOI) camera is sent to the trained model to generate a test descriptor, which is compared with a reference descriptor to obtain a similarity score. After comparing the results of this method with a popular traditional similarity matching method SIFT, we find that in the most cases this approach is more effective and more flexible than the traditional image processing-based methods, and it can be used to detect different types of defects with minimum customization.
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