PB-YOLOv7 Pedestrian Detection Method for Dense Scenes

doi:10.3969/j.issn.1006-2475.2025.09.002

Abstract

Abstract:
Abstract： Aiming at the problems of low detection speed and inaccurate localization of the dense crowd detection process in complex backgrounds， a dense scene pedestrian detection method PB-YOLOv7 is proposed. Firstly， the PP-LCNet-based network is used instead of the original backbone feature network to reduce the complexity of the model computing process by utilizing the depth-separable convolution. Secondly， the feature fusion idea of the bidirectional feature pyramid network BiFPN is used to enhance the feature fusion network’s utilization of the deeper， shallower， and the original feature information， and to reduce the loss of the important feature information in the process of convolution. Finally， the CBAM attention module is introduced to the junction location to enhance the feature extraction capability of the algorithm in order to make the network more concerned about the effective information. The experimental results show that the improved algorithm improves the mAP by 0.7 percentage points and the FPS value by 1.6 f/s compared with the original algorithm under the publicly available dense pedestrian dataset WiderPerson， realizing the balance between detection accuracy and detection speed.

Key words: Key words： intensive pedestrian detection, YOLOv7, PP-LCNet, bidirectional feature pyramid network, attention mechanism

CLC Number:

TP391.4

GUO Jinhao, WANG Fengping, WANG Haoqi. PB-YOLOv7 Pedestrian Detection Method for Dense Scenes[J]. Computer and Modernization, 2025, 0(09): 14-19.

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