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

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.

A Lightweight Improved U-Net with Shallow Features Combination and Its Application to Defect Detection H. Wu, X.-K. Sun*, Y.-J. Xiong, Wuhan University Journal of Natural Sciences, 2020, 25 (5): 461-468