Research on outdoor pedestrian detection algorithm based on improved YOLOv7-tiny
In high-density traffic scenarios for pedestrian detection,detection algorithms usually miss obscured and distant fuzzy pedestrians,while failing to balance detection accuracy and speed.To address these problems,an improved outdoor pedes-trian detection algorithm based on YOLOv7-tiny is proposed.The algorithm introduces the SENet attention mechanism,which sup-presses irrelevant information as a way to improve the ability of the feature map to express information,and at the same time en-hances the extraction of pedestrian target features.In order to better recognize the edges and overlapping of targets and improve the regression accuracy,SIoU is used instead of CIoU to improve the detection rate in the case of occlusion.According to the experi-ments on the WiderPerson dataset,the average detection accuracy is improved by 2 percentage comparing with the YOLOv7-tiny detection algorithm under the premise of guaranteeing the detection speed.The experimental results show that the improved algo-rithm can significantly improve the detection performance.
NETL
NSTL
万方数据
