Abstract: To effectively address the issues of occlusion and missed detection in crowded scenes and further enhance both accuracy and detection speed, a lightweight dense crowd detection algorithm that improves upon YOLOv8 is proposed. Firstly, RFAConv (Receptive Field Attention Convolution) is employed to replace some of the 3×3 convolutional blocks in the YOLOv8 backbone network, thereby strengthening the network’s ability to extract features and capture detailed feature information. Secondly, the cross-scale feature fusion module(CCFM) is utilized to aggregate information across scales through a cross-scale feature fusion structure, enhancing the model’s adaptability to scale variations and enabling it to precisely locate objects of different sizes simultaneously. Additionally, the lightweight detection head(LGD) is adopted, replacing batch normalization (BN) with group normalization (GN) to improve the detection head’s performance in localization and classification. Experimental results demonstrate that, compared to the original YOLOv8 algorithm, the improved algorithm achieves 0.4 percentage points increase in mAP@0.5 and  0.5 percentage points increase in mAP@0.5:0.95 on the WiderPerson dataset, while reducing the parameter count by 1.6×106 and the computational load by 2.4 GFLOPs. Through ablation experiments and comparative model experiments, the effectiveness and generalization capability of the proposed algorithm are validated. It improves the issues of occlusion and missed detection in dense crowds while meeting the requirements for both lightweight design and accuracy.

Key words: light weight, dense pedestrian, object detection, YOLOv8 algorithm