计算机工程与应用2026，Vol.62Issue(1)：124-139,16.DOI:10.3778/j.issn.1002-8331.2503-0168
MFE-YOLO:复杂场景下多尺度特征增强行人检测算法
MFE-YOLO:Multi-Scale Feature Enhancement Algorithm for Pedestrian Detection in Com-plex Scenes
摘要
Abstract
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.关键词
深度学习/行人检测/MetaFormer架构/多尺度特征增强/YOLOv8Key words
deep learning/pedestrian detection/MetaFormer architecture/multi-scale feature enhancement/YOLOv8分类
信息技术与安全科学引用本文复制引用
黄德意,黄德启,黄海峰,刘振航..MFE-YOLO:复杂场景下多尺度特征增强行人检测算法[J].计算机工程与应用,2026,62(1):124-139,16.基金项目
新疆维吾尔自治区自然科学基金(2022D01C430) （2022D01C430）
国家自然科学基金(51468062). （51468062）
