YOLO-CPEE: A lightweight and efficient dense pedestrian detection algorithm based on YOLO11

Abstract

Pedestrian detection in dense scenes faces significant challenges due to the mutual occlusion between pedestrians and the small proportion of distant pedestrians in the image. In this paper, a pedestrian detection algorithm named YOLO-CPEE is proposed to address these challenges for dense scenes. First, the cascaded group attention is integrated into the C2PSA module resulting in a new module (C2CGA) to achieve a good balance between detection efficiency and accuracy. Second, a high-resolution feature layer (P2) is introduced in YOLO-CPEE to better capture shallow pedestrian feature details and location information of small objects. Third, to enhance the ability of multi-scale feature extraction, the cross-scale feature interaction and adaptive weight allocation are achieved through efficient multi-scale attention (EMA). Moreover, a new lightweight dynamic detection head (Detect-Efficient) is designed, which significantly improves the object detection ability while reducing the model parameters. To test the proposed YOLO-CPEE algorithm, a self-built dataset DensePed-LR and the public dataset CityPersons are taken as reliable evaluation benchmarks, which both are high-density crowd dedicated datasets containing challenging scenes for testing. On DensePed-LR, YOLO-CPEE achieves improvements of 4.1% in recall and 3.2% in mAP@0.5:0.95, along with a faster inference speed of 135.3 FPS relative to the baseline. On CityPersons, YOLO-CPEE further achieves improvements of 3.9% in recall and 3.2% in mAP@0.5:0.95, while reducing the model size by 2.6%.