REMW‐YOLO: A Balanced Dense Pedestrian Detection Model With Efficient Feature Extraction and Fusion

Dense pedestrian detection plays a crucial role in intelligent security and video analysis, yet it faces challenges such as occlusion, scale variations, and difficulties in feature extraction. To address these, this study proposes REMW‐YOLO, an efficient model built on YOLO11n that balances performance and computational efficiency. To tackle occlusion, we design the C3k2RD module, replacing the traditional C3k2 structure. This module integrates RFAConv and DynamicConv to form a complementary “local enhancement–global adaptation” mechanism, improving detection of occluded pedestrians. We also present the efficient dual‐channel fusion feature pyramid network (EDFFPN), a lightweight architecture using dual‐channel fusion mechanisms to preserve detailed features while addressing multiscale detection and reducing computational cost. Additionally, we develop the multiscale lightweight decoupled detection head (MSLDH), which enhances feature expression for dense targets without increasing complexity. Finally, we propose the Wise‐Inner‐MPDIoU loss function to address sample imbalance in dense scenes, resulting in faster convergence and more precise localization. Experiments on the WiderPerson, CrowdHuman, and CityPersons datasets show that REMW‐YOLO improves mAP@0. 5 by 2. 11%, 1. 81%, and 2. 90%, and mAP@0. 5: 0. 95 by 1. 81%, 2. 36%, and 2. 19%, respectively, over YOLO11n, with overall performance significantly surpassing other methods. Cross‐dataset validation confirms the model’s generalization. REMW‐YOLO offers a balanced, efficient solution for dense pedestrian detection.