Abstract: To address challenges of detecting multi-scale objects under adverse weather conditions such as fog, including severe occlusion, low image visibility, and frequent false positives and missed detections, a foggy weather object detection algorithm based on YOLOv11, named MFA-YOLO (Multi-Scale Frequency-Aware Feature Alignment YOLO), was proposed. Firstly, the C3k2_Multi-Scale Edge Feature Enhancement (C3k2_MSEFE) module was designed to replace original C3k2 module in YOLOv11, enhanced network’s ability to extract features from multi-scale objects. Secondly, the Lightweight Frequency-Aware Feature Pyramid Network (LFFPN) was designed to address the issue of inaccurate bounding box localization caused by low contrast in foggy images, thereby improved both accuracy and robustness of detection. Third, the Dynamic Feature Alignment Detection Head (DFADHead) was introduced to strengthen model’s capability to detect overlapping and occluded objects through mechanisms such as dynamic convolution and task decomposition, and improved detection performance in dense scenes. Experimental results demonstrate that on the RTTS dataset, the proposed algorithm achieves a mAP@50 of 76.5% and a mAP@50:95 of 52.6%, representing improvements of 2.7 and 1.9 percentage points over the baseline model, respectively. Meanwhile, the number of parameters and model size are reduced by 23.4% and 15.3%, respectively. Furthermore, evaluations on two foggy datasets with different fog densities synthesized from Cityscapes dataset show that the proposed algorithm achieves mAP@50 scores of 48.5% and 46.7% which are 2.4 and 3.5 percentage points higher than YOLOv11s, respectively. These results further validate adaptability and effectiveness of the proposed algorithm across various foggy weather scenarios.