DPDN-YOLOv8: A Method for Dense Pedestrian Detection in Complex Environments

Yue Liu, Linjun Xu, Baolong Li, Zifan Lin () and Deyue Yuan
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Yue Liu: College of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
Linjun Xu: College of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
Baolong Li: College of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
Zifan Lin: Department of Electrical and Electronic Engineering, School of Engineering, University of Western Australia, Crawley, Perth, WA 6009, Australia
Deyue Yuan: College of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China

Mathematics, 2025, vol. 13, issue 20, 1-21

Abstract: Accurate pedestrian detection from a robotic perspective has become increasingly critical, especially in complex environments such as crowded and high-density populations. Existing methods have low accuracy due to multi-scale pedestrians and dense occlusion in complex environments. To address the above drawbacks, a dense pedestrian detection network architecture based on YOLOv8n (DPDN-YOLOv8) was introduced for complex environments. The network aims to improve robots’ pedestrian detection in complex environments. Firstly, the C2f modules in the backbone network are replaced with C2f_ODConv modules integrating omni-dimensional dynamic convolution (ODConv) to enable the model’s multi-dimensional feature focusing on detected targets. Secondly, the up-sampling operator Content-Aware Reassembly of Features (CARAFE) is presented to replace the Up-Sample module to reduce the loss of the up-sampling information. Then, the Adaptive Spatial Feature Fusion detector head with four detector heads (ASFF-4) was introduced to enhance the system’s ability to detect small targets. Finally, to accelerate the convergence of the network, the Focaler-Shape-IoU is utilized to become the bounding box regression loss function. The experimental results show that, compared with YOLOv8n, the mAP@0.5 of DPDN-YOLOv8 increases from 80.5% to 85.6%. Although model parameters increase from 3 × 10 6 to 5.2 × 10 6 , it can still meet requirements for deployment on mobile devices.

Keywords: pedestrian detection; YOLOv8; ODConv; CARAFE (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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