Knowledge distillation-based unsupervised industrial image anomaly detection method and system

Abstract

The invention discloses an unsupervised industrial image anomaly detection method and system based on knowledge distillation, and belongs to the technical field of industrial image processing. Comprising a training stage and a testing stage, and is composed of multi-scale knowledge distillation and multi-scale anomaly fusion, the multi-scale knowledge distillation comprises a teacher network and a student network, hard case samples are dynamically mined by using adaptive hard case mining, and the student network is optimized by using pixels among the hard case samples and context similarity. In the training stage, knowledge distillation from a teacher network to a student network is carried out only by using a normal industrial image, and iterative optimization is carried out on student network parameters, so that the normal industrial product image depth features extracted by the student network and the teacher network are similar; in a test stage, depth features of a test image are respectively extracted, and regression errors between the features can be used for image anomaly segmentation and detection. According to the method, the performance of unsupervised industrial image anomaly detection is effectively improved, the labor cost is reduced, and the automation and intelligence level of production line quality inspection is improved.

Description

technical field [0001] The invention belongs to the technical field of industrial image processing, and further relates to an unsupervised industrial image anomaly detection method and system based on knowledge distillation. Background technique [0002] The automatic detection of industrial product quality is very important. Industrial image anomaly detection technology can improve the product quality detection performance and production efficiency of the production line. At present, in most industries, such as steel, textiles, semiconductor industries, etc., the inspection of product quality still mainly relies on manual experience, and inspectors are prone to fatigue after long-term work, resulting in false inspections and missed inspections. The industrial image anomaly detection technology based on image processing has thus been developed, which can replace manual labor for effective industrial product quality inspection. The main process of traditional image processin...

AI Technical Summary

Problems solved by technology

[0003] However, due to the low frequency of anomalies in industrial production lines, the collected normal data is much larger than abnormal data, so unsupervised deep learning anomaly detection methods are of great significance

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