Object　detection　in　autonomous　driving　requires　high　accuracy　and　speed　in　different　weather.　At　present,　many　CNN-based　networks　have　achieved　high　accuracy　on　academic　datasets,　but　their　performance　disastrously　degrade　when　images　contain　various　kinds　of　noises,　which　is　fatal　for　autonomous　driving.　In　this　paper,　we　propose　a　detection　network　based　on　shifted　windows　Transformer　(Swin　Transformer)　called　SwinCGH-Net,　with　a　kind　of　new　detector　head　based　on　lightweight　convolution　attention　module,　which　makes　full　use　of　the　attention　mechanism　in　both　feature　extraction　and　detection　stages.　Specifically,　we　use　Swin　Transformer　as　backbone　to　extract　feature　in　order　to　obtain　effective　information　from　a　small　amount　of　pixels　as　well　as　integrate　global　information.　Then　we　further　improve　the　robustness　of　the　network　through　the　detector　head　contained　lightweight　attention　block　S-CBAM.　Furthermore,　we　use　Generalized　Focal　Loss　to　calculate　loss,　which　effectively　enhances　the　representation　ability　of　the　model.　Experiments　on　Cityscapes　and　Cityscapes-C　datasets　demonstrate　the　superiority　and　effectiveness　of　our　method　in　different　weather　condition.　With　the　increasing　level　of　weather　noise,　our　method　shows　strong　robustness　compared　with　previous　method,　especially　in　small　object　detection.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.