Pedestrian detection based on multi-scale features and mutual supervision
Aiming at the high false negative rate and low accuracy in crowded scenes,a pedestrian de-tection network based on multi-scale features and mutual supervision is proposed.To effectively extract pedestrian feature information in complex scenes,a network combining PANet pyramid network and mixed dilated convolutions is used to extract feature information.Then,a mutual supervision detection network for head-body detection is designed,which utilizes the mutual supervision of head bounding bo-xes and full-body bounding boxes to obtain more accurate pedestrian detection results.The proposed network achieves 13.5%MR-2 performance on CrowdHuman dataset,with an improvement of 3.6%compared to the YOLOv5 network,and a simultaneous improvement of 3.5%in average precision(AP).On CityPersons dataset,it achieves 48.2%MR-2 performance,with 2.3%improvement com-pared to the YOLOv5 network,and a simultaneous improvement of 2.8%in AP.The results indicate that the proposed network demonstrates good detection performance in densely crowded scenes.
国家科技期刊平台
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