Wooden floor anomaly detection method by Generative Adversarial Net-works
In the process of abnormal detection of wooden boards,due to the large color span of wooden floors,it is difficult to distinguish between texture backgrounds and defects,and there are also problems such as a very small proportion of defects and unforeseeable defects,which brings great difficulties to image-based abnormal detection of wooden boards.This article propo-ses a new unsupervised anomaly detection method based on memory enhanced generative adver-sarial networks,which can detect abnormal defects using only a large number of defect free sam-ples.This method utilizes the skip connection structure of U-Net to improve the reconstruction effect of the model;Secondly,an attention mechanism is proposed in the discriminator section to generate images with more complex structures and more accurate details;At the same time,adding a memory enhancement module enables the model to remember the features of normal data,thereby increasing the reconstruction error of abnormal data and enhancing the discrimina-tion performance.The experimental results show 0.962that the proposed method has higher ac-curacy and Fl score compared to classic unsupervised detection models such as GANomaly,skip GANoamly,and Anogan.
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维普
