Multi-Scale Enhanced Deep Network for Road Detection

Xiaoyan Lu, Yanfei Zhong, Ji Zhao

Abstract: Road detection is a hot research topic in the very high resolution (VHR) remote sensing field and has been applied in various practical applications. Many deep-learning based methods have been used to detect roads and achieved good performance. In this paper, a multi-scale enhanced road detection framework (DenseUNet) is proposed which based on the densely connected convolutional networks (DenseNet) and U-Net. The U-Net has strong capabilities of preserving spatial details due to its skip connections, and the DenseNet can better optimize the deep network. Meanwhile, atrous spatial pyramid pooling (ASPP) is employed to effectively capture multi-scale features for road detection. Finally, a public road dataset was used to verify the proposed approach, compared with other state-of-the-art methods. The proposed method achieve the best performance, which illustrates its superiority.