Abstract: 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.