To address challenges of detecting multi-scale objects under adverse weather conditions such as fog weather， including severe occlusion， low image visibility， leading to frequent false positives and missed detections， a object detection algorithm based on improved YOLOv11 in foggy weather， named MFA-YOLO （Multi-scale frequency-aware Feature Alignment YOLO）， was proposed. First， the multi-scale edge feature enhancement module C3k2_MSEFE （C3k2_Multi-Scale Edge Feature Enhancement） was designed to replace the C3k2 module in YOLOv11 network， so as to enhance network’s ability to extract features from multi-scale objects. Second， 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， so as to improve both accuracy and robustness of detection. Finally， the Dynamic Feature Alignment Detection Head （DFADHead） was designed to strengthen capability to detect overlapping and occluded objects in dense scenarios through mechanisms such as dynamic convolution and task decomposition. Experimental results demonstrate that on the RTTS dataset， the proposed algorithm achieves an mAP@50 of 76.5% and an mAP@50：95 of 52.6%， representing improvements of 2.7 and 1.9 percentage points compared to the baseline model， respectively. Meanwhile， the number of parameters and model size are reduced by 15.3% and 23.4%， respectively. Furthermore， verification results on two types of foggy images with different fog concentrations synthesized from Cityscapes dataset show that the proposed algorithm achieves the mAP@50 of 48.5% and 46.7%， respectively， which are 2.4 and 3.5 percentage points higher than those of YOLOv11s， respectively， further validating the adaptability and effectiveness of the proposed algorithm in various foggy weather scenarios.