no code implementations • 1 Jun 2023 • Anzhu Yu, Wenjun Huang, Qing Xu, Qun Sun, Wenyue Guo, Song Ji, Bowei Wen, Chunping Qiu
The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade.
no code implementations • 28 Apr 2023 • Xin Chen, Anzhu Yu, Qun Sun, Wenyue Guo, Qing Xu, Bowei Wen
However, obtaining bi-phase images for the same area is difficult, and complex post-processing methods are required to update the existing databases. To solve these problems, we proposed a road detection method based on semi-supervised learning (SRUNet) specifically for road-updating applications; in this approach, historical road information was fused with the latest images to directly obtain the latest state of the road. Considering that the texture of a road is complex, a multi-branch network, named the Map Encoding Branch (MEB) was proposed for representation learning, where the Boundary Enhancement Module (BEM) was used to improve the accuracy of boundary prediction, and the Residual Refinement Module (RRM) was used to optimize the prediction results.