MFE-YOLO:复杂场景下多尺度特征增强行人检测算法OA

To address the issues of high miss rate and insufficient false detection suppression in pedestrian detection under complex scenes caused by multi-scale distribution,dense occlusion,and environmental noise interference,this paper proposes MFE-YOLO,a multi-scale feature-enhanced pedestrian detection algorithm.First,an efficient global feature extraction module(EGM)is designed by integrating MetaFormer architecture with depthwise separable convolu-tion to enhance the backbone network's capability in capturing fine-grained pedestrian features.Additionally,a hybrid multi-level feature fusion network(MLHS-FPN),driven by a mixed local feature selection mechanism(MLCA),is con-structed to dynamically fuse local details and global contextual information through cross-layer gating units,thereby improving the robustness of multi-scale pedestrian representations.A shallow P2 detection head is introduced to leverage high-resolution shallow features and deep semantic information for small target detection.Finally,the Wise-inner-PIoUv2 loss function is adopted to suppress harmful gradients from low-quality samples.Experiments on the CityPersons dataset demonstrate that MFE-YOLO achieves significant improvements over the baseline model,with F1-score and AP50 increasing by 3.7 percentage points and 6.5 percentage points,respectively,while reducing model parameters by 37%and size by 35.8%.The algorithm maintains robust detection performance in more complex scenarios,as validated on the BDD100K dataset.Results indicate that MFE-YOLO exhibits high accuracy for multi-scale pedestrian detection in chal-lenging environments.