Detecting Object Defects with Fusioning Convolutional Siamese Neural Networks

Recently, the combination of deep learning algorithms with visual inspection technology allows differentiating anomalies in objects mimicking human visual inspection. While it offers precise and persistent monitoring with a minimum amount of human activity but to apply the same solution to a wide variety of defect types is challenging. In this paper, a new convolutional siamese neural model is presented to recognize different types of defects. One advantage of the proposed convolutional siamese neural network is that it can be used for new object types without re-training with much better performance than other siamese networks: it can generalize the knowledge of defect types and can apply it to new object classes. The proposed approach is tested with good results on two different data sets: one contains traffic signs of different types and different distortions, the other is a set of metal disk-shape castings with and without defects.

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