基于 YOLOv4的行人检测算法.

Aiming at the YOLOv4 model's difficulty in dealing with occluded pedestrians in real scenarios, this paper made improvements in ensuring the real-time performance of the YOLOv4 model and applied the YOLOv4 model to pedestrian detection. In order to improve the model's ability to detect occluded pedestrians, the model adopted the K-means ++ clustering algorithm to re-design the priori frames applicable to the pedestrian target sizes, and introduced the exclusion loss function term to maximise the distance between the candidate frames and the neighbouring real frames of non-mate-hing targets, and minimise the overlap ratio between the candidate frames and the real frames of other targets. The improved model was experimented on the challenging datasets CrowdHuman and Caltech, and the experimental results verified the effectiveness of it. Finally the model has been applied to video pedestrian detection in real scenarios, which also verified the effectiveness of the improvements in this paper. [ABSTRACT FROM AUTHOR]