Nighttime Image Semantic Segmentation Based on Image Style Confrontation and Two-way Category Optimization
ZHOU Huaping
LI Meiguang
Abstract：In order to solve the problem of semantic information transmission loss and lack of attention to small frequency categories in the semantic segmentation of nighttime images,an algorithm model based on image style confrontation and two-category optimization network architecture(ITA)was proposed.Firstly,the architecture of adversarial learning was adopted,so that the image sharing information could be used efficiently,and the transmission of semantic information was more complete.Then,the framework also adopted the two-way category guidance(TCG)strategy to reallocate the class weights,and the guidance model paid more attention to the small-frequency classes.Finally,the mean intersection over union(MIoU)in the dark Zurich dataset increased to 60.1%.Meanwhile,the effectiveness of each module was also demonstrated by ablation experiments.The ITA model framework could accurately segment the nighttime road image,which could provide reference for the night autonomous driving task.
Keywords：nighttime imagesemantic segmentationadversarial networkdomain adaptationimage alignmentdata balancingstyle conversion
Publication Date：2024-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 69-74 )