Abstract: Aiming at the high false negative rate and low accuracy in crowded scenes, a pedestrian detection 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 boxes 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 compared 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.

Key words: crowded scene, pedestrian detection, multi-scale network, mutual supervision