Dual-Branch Enhancement and Multi-Modal Fusion for Low-Light Visible Polarization Image Object Detection in Dense Smog Environments

In scenarios with heavy smog, the accuracy of object detection in low-light visible polarization images significantly decreases. To address this issue, we propose a dual-branch enhancement and multi-modal fusion network for object detection in low-light visible polarization images in dense smog environments. Specifically, the network consists of an image enhancement stage and an object detection stage. In the image enhancement stage, a dual-branch enhancement structure comprising greyscale feature map prediction and atmospheric light transmission network is proposed to remove noise from the images and enhance texture information, jointly generating enhanced visible polarization images. In the object detection stage, feature maps of the enhanced visible polarization images and the degree of visible polarization images are fused, and their fused texture-enhanced feature maps are fed into the detection module for object detection. Additionally, we have collected a dataset of low-light visible polarization images under real smog conditions. Extensive experiments demonstrate that our method can generate visually improved enhanced images and significantly increase detection accuracy and the number of detected objects in low-light and dense smog environments.