To address insufficient real-time detection accuracy of standard YOLO models under adverse weather, we propose PFOD-Net, a multi-scale detector based on polarimetric features and an improved YOLOv8n. Compared with traditional intensity imaging, polarimetric imaging extracts optical information from target scenes more effectively; when coupled with an appropriate feature selection mechanism, it significantly enhances detection and recognition performance in complex environments. Experimental results on the Polar LITIS dataset demonstrate that PFOD-Net significantly outperforms YOLOv8 in both accuracy and speed: mAP@0.5 (mean Average Precision) increased from 33.5% to 81.7%—an increase of 48.2 percentage points. Notably, the detection performance of PFOD-Net is substantially reinforced in complex conditions like fog and haze. By innovatively integrating polarimetric information, lightweight architectures, and the fusion of max-pooling and average-pooling, this method provides an effective solution for object detection and recognition in adverse weather.