Low-Light Image Semantic Segmentation Method Based on Vector Symbolic Architecture-Domain Adaptation Network

In view of the insufficient illumination of low-light images in semantic segmentation, there are phenomena such as low image brightness, contrast, and signal-to-noise ratio, which makes semantic segmentation of low-light images more difficult. A low-light image semantic segmentation method based on vector symbolic architectures domain adaptation network is proposed. The method first integrates the image translation network based on vector symbolic architectures and the domain adaptive semantic segmentation network. The image translation network translates normal illumination images into low-light images, reducing style differences between domains and improving the segmentation accuracy of low-light images. Then, a layered feature mapping (LFM) module is proposed in the image translation network. The LFM module can better map the multi-layer feature vectors of the semantic segmentation network encoder to the VSA space, making the translated image more similar to the low-light image, thereby improving the segmentation accuracy of the segmentation network. Finally, a cross domain rare class mixing (CDRCM) method is proposed in the domain adaptive semantic segmentation network. CDRCM obtains rare classes in the low-light domain based on the pseudo- label distribution of low-light images during domain adaptation, and biases towards these rare classes during cross-domain mixing, thereby improving the segmentation accuracy of rare classes in the low-light domain. The experimental results on Cityscapes→Dark Zurich and Cityscapes→ACDC-night indicated that the proposed method in the paper achieved improvements of 8.2 percentage points and 7.2 percentage points, respectively, in mean intersection-over-union compared to the baseline DAFormer, the experimental results show the effectiveness of the method.