Anomaly detection for surface of laptop computer based on PatchCore GAN algorithm

Timely detection of notebook appearance defects is an important means to prevent products from being delivered to customers before leaving the factory. In industrial production, more emphasis is placed on fast and accurate detection methods, but the existing difficulties: 1. Defect samples are rare and difficult to obtain; 2. In high-resolution images, there are slight differences between abnormal samples and normal samples; 3. Slowly detection and insufficient accuracy. The existing methods mainly use a large amount of abnormal samples, so it is difficult to extend to the field of notebook appearance anomaly detection. To solve this problem, we designed a method that firstly uses unsupervised PatchCore which the algorithm was trained on normal samples and Defect GAN is used in test phase. To create a large number of verisimilitude abnormal samples and test these samples with PatchCore. On TKP-Surface datasets, the AUROC score of image-level anomaly detection achieves 96.1 %, which meets the requirements of industrial applications.