A Lightweight Improved U-Net with Shallow Features Combination and Its Application to Defect Detection

• 作者: SUN Xiankun WU Hong XIONG Yujie
• 来源期刊: 武汉大学自然科学学报（英文版）
• 学科: 工学
• 年，卷（期）: 2020,（5）
• 所属期刊栏目: Information Technology
• 页码范围: 461-468
• 页数: 8页
• 分类号: TP391
• 语种: 英文
• DOI: 10.19823/j.cnki.1007-1202.2020.0058

摘要

In order to solve the problems of shallow features loss and high computation cost of U-Net,we propose a lightweight with shallow features combination (IU-Net).IU-Net adds several convolution layers and short links to the skip path to extract more shallow features.At the same time,the original convolution is replaced by the depth-wise separable convolution to reduce the calculation cost and the number of parameters.IU-Net is applied to detecting small metal industrial products defects.It is evaluated on our own SUES-Washer dataset to verify the effectiveness.Experimental results demonstrate that our proposed method outperforms the original U-Net,and it has 1.73％,2.08％ and 11.2％ improvement in the intersection over union,accuracy,and detection time,respectively,which satisfies the requirements of industrial detection.