Lightweight Pedestrian Detection Algorithm for Dense Crowd Scenes based on YOLOv7-tiny
In response to the challenges posed by existing high-precision pedestrian detection models,which require substantial resources and are thus difficult to apply in edge computing scenarios,this paper proposes a lightweight real-time dense pedestrian detection algorithm suitable for edge GPU devices.This algorithm reduces the impact of redundant information on detection performance by integrating full-dimensional dynamic convolution in the detection head,and enhances the algorithm's ability to distinguish between the target to be detected and the background through optimizing the loss function.Experimental results demonstrate that in pedestrian detection tasks within densely populated scenes,this algorithm improves accuracy by 4.1 percentage points compared to the baseline algorithm YOLOv7-tiny presented in this paper,proving that it can achieve accurate dense crowd detection in edge computing scenarios.
NETL
NSTL
万方数据
