基于改进ConvNeXt的软塑包装表面异常检测算法

Surface Anomaly Detection Algorithm of Flexible Plastic Packaging Based on Improved ConvNeXt

针对人工检测软塑包装存在速度慢且易受主观因素影响造成误检等问题,以及基于深度学习的机器视觉中负样本数量少、获取困难等客观原因,本文以抽纸外表面为研究对象,提出了基于ConvNeXt的非对称对偶网络的抽纸包装表面质量检测方法。首先根据工业现场采集图片状况,使用机器视觉中基于阈值分割及图像滤波的方法对图像进行前景提取等预处理;之后,根据图片特征及异常特点,构建异常检测网络结构;最后将预处理后的图片构建为训练集与测试集,训练并测试抽纸包装表面质量检测网络。实验结果表明,在抽纸外包装表面缺陷检测中,图片级AUROC为99.75%,像素级AUROC为99.37%,单张检测时间为45 ms,满足工业实时性检测要求。

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 im‐ages 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.