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

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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.

U-Netdepth-wise separable convolutionshallow features combinationdefect detection

WU Hong、SUN Xiankun、XIONG Yujie

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School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Youth Fund of National Natural Science Foundation of ChinaYouth Fund of National Natural Science Foundation of ChinaShanghai Young Science and Technology Talents Sailing Programand Fund Project of Shanghai Science and Technology Commission

618012866200615019YF141840016dz1206002

2020

10.19823/j.cnki.1007-1202.2020.0058

武汉大学自然科学学报(英文版)
武汉大学

武汉大学自然科学学报(英文版)

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影响因子：0.066
ISSN：1007-1202
年,卷(期)：2020.25(5)
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