A　dual-modality　object　detection　algorithm,　based　on　the　dual-scale　convolutional　block　attention　module　(CBAM),　is　addressed　to　tackle　challenges　posed　by　adverse　weather　conditions　and　low　lighting　for　visual　object　detection　algorithms　based　on　deep　learning.　The　algorithm　aims　to　improve　the　robustness　and　accuracy　of　object　detection　in　challenging　environments　by　fusing　features　from　vision　and　millimeter　wave　(mmWave)　radar.　It　utilized　a　dual-branch　one-stage　architecture,　with　the　image　branch　using　a　pre-trained　CSPDarkNet53　backbone　network　to　extract　image　features　and　the　radar　branch　employing　a　voxel-based　radar　feature　generation　network　to　extract　radar　features.　The　proposed　dual-scale　CBAM　feature　fusion　module　integrated　radar　and　visual　features　before　and　after　the　neck　network.　Finally,　a　decoupled　detection　head　was　deployed　to　classify　and　locate　objects.　The　effectiveness　and　superiority　of　the　proposed　fusion　detection　algorithm　were　validated　by　comparative　and　ablation　experiments　conducted　on　the　nuScenes　dataset　in　challenging　environments.　©　2025　Beijing　University　of　Technology.　All　rights　reserved.