Adaptive Semantic Segmentation of Traffic Scenes via Frequency Domain Analysis

High-precision semantic segmentation is an important research topic in the communities of computer vision and intelligent transportation. The existing unsupervised domain adaptation methods based on image translation often lead to artifacts and structural distortions. To overcome this problem, a novel adaptive semantic segmentation method of traffic scenes via frequency domain analysis is proposed. Firstly, we leverage the frequency domain space to decouple style and semantic features. The Fast Fourier Transform is applied to achieve structural-preserving style alignment. Subsequently, a content enhancement module is proposed based on the Wavelet transform, which utilizes the original source images to correct and enhance high-frequency structural and semantic details. Furthermore, a convolutional enhancement attention module is proposed, which utilizes depthwise separable convolution to capture more local details. The experimental results based on the GTA5→Cityscapes and SYNTHIA→Cityscapes tasks have respectively attained state-of-the-art mIoU of 76.4 and 67.7, convincingly demonstrating the effectiveness of the methods.