Author(s)

Ranning Deng ¹

Affiliation(s)

¹ School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China

Corresponding Author

Ranning Deng

ABSTRACT

Real-time pedestrian detection, as a key technology in the field of computer vision, has broad application demands in intelligent surveillance, autonomous driving, robot navigation, and other areas. To address the problem that high-computational-power models are difficult to deploy on edge devices, this paper proposes a real-time pedestrian detection scheme based on the lightweight YOLOv5-tiny model. The study uses a pedestrian subset of the COCO dataset for model training, optimizes the anchor box dimensions through the K-means clustering algorithm to adapt to pedestrian target characteristics, and tests the model performance on ordinary CPU and GPU environments. Experimental results show that the optimized model can achieve a detection speed of 23.6 FPS with a recall rate of 82.3% on the Intel Core i7-10700 CPU; on the NVIDIA GTX 1650 GPU, the frame rate increases to 45.2 FPS and the recall rate rises to 84.7%, which can meet the real-time and detection accuracy requirements in low-computational-power scenarios. 

KEYWORDS

Computer Vision; YOLOv5-tiny; Pedestrian Detection; Lightweight Model

CITE THIS PAPER

Ranning Deng, Real-Time Pedestrian Detection System Based on YOLOv5-tiny. Journal of Electronics and Information Science (2025) Vol. 10: 128-134. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2025.100215.

REFERENCES

[1] Bochkovskiy, Alexey, Chien-Yao Wang, and Hong-Yuan Mark Liao. "Yolov4: Optimal speed and accuracy of object detection." arxiv preprint arxiv:2004.10934 (2020).
[2] Horvat, Marko, Ljudevit Jelečević, and Gordan Gledec. "A comparative study of YOLOv5 models performance for image localization and classification." Central European Conference on Information and Intelligent Systems. Faculty of Organization and Informatics Varazdin, 2022.
[3] Luo, Xiangjie, et al. "A lightweight YOLOv5-FFM model for occlusion pedestrian detection." arxiv preprint arxiv:2408.06633 (2024).
[4] Ye, Meng, Hao Wang, and Hang Xiao. "Light-YOLOv5: A lightweight algorithm for improved YOLOv5 in PCB defect detection." 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2023.
[5] Lin, Tsung-Yi, et al. "Focal loss for dense object detection." Proceedings of the IEEE international conference on computer vision. 2017.

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