关键词: 关键词：深度学习, 抽纸外包装, 对偶网络, 异常检测

Abstract: Abstract： As for the artificial detection of flexible plastic packaging is slow and easily influenced by subjective factors which bring the problems such as error checking，as well as machine vision based on the deep learning only got a few of negative sample  which is difficult to obtain， the article proposed a ConvNeXt based asymmetirc dual network method to detect the outer surface of the tissue which is taken as the research object. Firstly， the method of machine vision based on threshold segmentation and image filtering is used to preprocess the image foreground extraction and correction， according to the situation of the industrial field images collected. Then， the anomaly detection network structure is constructed according to the characteristics of images. Finally， the preprocessed images were constructed as data sets to train and test the surface quality detection network of tissue. As a result， the experiment shows that the image-level AUROC is 99.75%， the pixel-level AUROC is 99.37%， and the detection time is 45 ms. The result meets the requirements of industrial real-time detection.

Key words: Key words： deep learning, tissue, dual network, anomaly detection