(Invited) Domain Adaptation for the Semantic Segmentation of the Unmanned Surface Vehicle. (8th September 2020) - Zhan, Wenqiang; Xiao, Changshi; Haiwen, Yuan; Zou, Xiong; Chen, Qianqian; Yang, Tiantian
Abstract : Recently, deep learning has great development and is applied in various fields. But the performance of the network depends on a large amount of label data training. After training in a specific domain, a network model can have a good performance. But it may have a poor performance when transferred into a new domain. This paper addresses the visual semantic segmentation model transfer for the unmanned surface vehicle (USV) system. An adaptive cross-domain incremental learning method is proposed, which enables USV adaptive to navigate in unknown new environments. In our work, the knowledge of the trained segmentation network model and the feature information of the input image is used for the network incremental learning. First, the trained semantic segmentation network is used to classify each pixel of the input image. Then, pixels with high prediction probability are selected as seed points. At the same time, a convolution network for feature extraction is used to obtain the embedded feature space vector of each pixel. A mask centered on the seed point is generated based on the similarity between pixels in the feature space. The mask is labeled based on the segmentation network prediction. Multiple masks are fused to generate the label of the input image. Finally, the automatically generated labels are used to update the network to realize the adaptability of the network in the new environment.