关键词: U-Net, 语义分割, MobileNet-V2, 深度可分离卷积

Abstract:

The U-Net model is large and accordingly has relatively slow speed on image processing. This drawback makes it difficult to satisfy the requirement of industrial real-time applications. On the consideration of the question above, this paper designs a light-weight full convolution neural network named LU-Net. In the proposed network, it integrates the thought of MobileNet-V2 into the U-Net framework. The depth separable convolution method in MobileNet-V2 is efficient to reduce the parameters and the computation complexity of the proposed network. The proposed network also reserves the advantages of normal convolution and bottleneck model. Accordingly, it is efficient to utilize the high-level features to keep the accuracy and reduce the processing time simultaneously. The experiments on hollow symbol dataset and DRIVE dataset indicate that, on the comparison with U-Net, the parameters of the proposed LU-Net is 0.59×106,which is 1.9% of the original model, and the processing speed is 5 times faster. Under the experimental environment, LU-Net takes only 25?ms to process a picture under the resolution of 360×270 size. LU-Net is a promising method for the industrial real-time applications on picture processing.

Key words: U-Net, semantic segmentation, MobileNet-V2, depth separable convolution