基于改进U-Net的零件缺陷分割标注  

Part defect segmentation and annotation based on improved U-Net

作　　者：金文倩 朱媛媛[1] 王笑梅[1] JIN Wenqian;ZHU Yuanyuan;WANG Xiaomei(College of Information,Mechanical and Electrical Engineering,Shanghai Normal University,Shanghai 201418,China)

机构地区：[1]上海师范大学信息与机电工程学院,上海201418

出　　处：《上海师范大学学报（自然科学版）》2022年第2期129-134,共6页Journal of Shanghai Normal University(Natural Sciences)

基　　金：上海市自然科学基金(17ZR1419800)。

摘　　要：提出一种以U-Net为基础,依据零件缺陷的特点对网络进行一系列改进的模型,以提升网络对零件缺陷的分割精度.首先在U-Net结构中的编码阶段,使用改进的残差网络Res2Net提高该阶段的特征提取能力;然后在网络编码器与解码器的中间部位增加空洞卷积,在不改变特征图尺寸的情况下增加感受野,降低误检率与漏检率;最后在U-Net的输出阶段与Mini U-Net进行结合,对原本的输出结果进行二次补丁,提高对微小缺陷的检测精度.实验结果表明,对MVTec数据集进行分割的F1-Score分数达到87.21%,时间为0.017 s,达到了良好的检测效果.A model based on U-Net network and a series of improvements to it according to the characteristics of part defects were proposed to improve the segmentation accuracy of part defects.Firstly,the improved residual network Res2Net was used in the coding stage of U-Net network structure to improve the feature extraction ability during this stage.Secondly,the hole convolution was added in the middle of the network encoder and decoder,and the receptive field was increased without changing the size of the characteristic image,so as to reduce the false detection rate and omission detection rate.Finally,in the output stage of U-Net,Mini U-Net was combined with to patch the original output results,so as to improve the detection accuracy of small defects.The experimental results showed that the F1-Score of MVtec dataset segmentation reached 87.21%and the time was 0.017 s,with which outstanding detection effect could be achieved.

关 键 词：图像分割 缺陷检测 U-Net Res2Net 空洞卷积 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]