Pedestrian Detection Algorithm of YOLOV8 Based on Feature Enhancement

In an effort to overcome the problem of insufficient network ability of YOLOV8 backbone feature extraction and poor feature fusion ability, a C2f module based on attention mechanism is introduced to carry to a new and higher level the model’s ability to express input features. Aiming at the feature fusion in improved YOLOV8, this paper first modifies the structure of FPN in order to effectively integrate the feature information extracted by backbone, so as to enhance the feature fusion ability of the model. In addition, this paper designs a new feature fusion structure to replace the existing PAN structure, make the feature map information input by FPN more reasonably utilized, so as to improve the overall detection accuracy of the model. The experimental results show that the improved YOLOV8 algorithm performs well on the CUHK dataset [email protected] Improved by 1.7%, and the detection speed remained basically unchanged, with excellent performance on WiderPerson, [email protected] Reaching 91.3%, it can quickly and accurately achieve pedestrian detection. In addition, this paper conducts relevant ablation experiments and comparative experiments to validate the effectiveness of each proposed improvement.