Citation

Ang GO, Xing-zhu L, Chen-xing XIA, Chun-jiong Z. J. Graphics 2023; 44(5): 890-898.

Copyright

(Copyright © 2023, China Association for Science and Technology)

DOI

10.11996/JG.j.2095-302X.2023050890

PMID

unavailable

Abstract

In response to the challenge of detecting small-scale, occluded pedestrians in dense scenes, where they are prone to being missed, we proposed an improved YOLOv8 detection algorithm. First, to address the issue of extracting features from small-scale pedestrians, a backbone network improved by deformable convolution was employed to enhance the feature extraction capability of the network, and an occlusion-aware attention mechanism was designed to enhance the visible part of the occluded pedestrian features. Second, to address imprecise localization of the detection head in dense pedestrian scenes, a dynamic decoupling head was designed to enhance attention to multi-scale pedestrian features, thereby improving the expression capability of the detection head. Finally, to address the problem of low model training efficiency, the regression loss that combined Wise-IoU with distributed focus loss was utilized for training, thereby enhancing the convergence ability of the model. Through the analysis of experimental results, the improved YOLOv8 algorithm demonstrated exceptional performance on two challenging and dense pedestrian datasets, namely CrowdHuman and WiderPerson, achieving an AP50 of 90.6% and 92.3% and an AP50:95 of 62.5% and 68.2%, respectively. In contrast to the original algorithm, the improvements were substantial, establishing robust competitiveness when compared with other state-of-the-art pedestrian detection models. The proposed algorithm exhibited a wide range of applications in dense pedestrian detection tasks.

Key words: YOLOv8, dense pedestrian detection, occlusion-aware attention, deformable convolution, dynamic decoupled head

Language: en