An improved dense pedestrian detection algorithm based on YOLOv8:MER-YOLO
In large-scale crowded places,abnormal crowd gathering occurs from time to time,which brings certain challenges to the dense pedestrian detection technology involved in application scenarios such as autonomous driving and large-scale public place crowd monitoring systems.The new generation of dense pedestrian detection technology requires higher accuracy,smaller computing overhead,faster detection speed and more convenient deployment.In view of the above requirements,a lightweight dense pedestrian detection algorithm MER-YOLO based on YOLOv8 is proposed,which first uses Mo-bileViT as the backbone network to improve the overall feature extraction ability of the model in pedes-trian gathering areas.The EMA attention mechanism module is introduced to encode the global infor-mation,further aggregate pixel-level features through dimensional interaction,and strengthen the de-tection ability of small targets by combining the detection head with 160×160 scale.The use of Repul-sion Loss as the bounding box loss function reduces the missed detection and misdetection of small tar-get pedestrians under dense crowds.The experimental results show that compared with YOLOv8n,the mAP@0.5 of the MER-YOLO pedestrian detection algorithm is improved by 4.5%on the Crowd Hu-man dataset and 2.1%on the WiderPerson dataset,while only 3.1×106 parameters and 9.8 GFLOPs,which meet the deployment requirements of low computing power and high precision.