Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Advances in Computer, Signals and Systems
  4. Vol 9, Issue 4, 2025

Research on Parking Lot Vehicle Counting Based on Simplified YOLOv3

Download as PDF

DOI: 10.23977/acss.2025.090411 | Downloads: 2 | Views: 52

Author(s)

Jiajie Qiu 1, Xiaoying Su 1

Affiliation(s)

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

Corresponding Author

Xiaoying Su

ABSTRACT

Aiming at the problems of low efficiency and high error rate in traditional parking lot vehicle counting which relies on manual inspection, this paper proposes a vehicle detection and counting method based on the simplified YOLOv3, specially adapted to the parking lot monitoring scenarios with fixed viewing angles and no occlusion. By simplifying the network structure of YOLOv3, the method improves the operation speed while ensuring the detection accuracy, thus meeting the real-time counting requirements. Experimental results show that the vehicle detection accuracy of this method reaches 95.2% on the self-built parking lot dataset, the counting error is controlled within 3%, and the operation efficiency is increased by 40% compared with the original YOLOv3 model. It can effectively realize the automatic and accurate counting of vehicles in parking lots, providing technical support for the intelligent upgrading of parking lot management systems.

KEYWORDS

Simplified YOLOv3; Vehicle Detection; Parking Lot Management; Vehicle Counting; Fixed View Angle

CITE THIS PAPER

Jiajie Qiu, Xiaoying Su, Research on Parking Lot Vehicle Counting Based on Simplified YOLOv3. Advances in Computer, Signals and Systems (2025) Vol. 9: 91-97. DOI: http://dx.doi.org/10.23977/acss.2025.090411.

REFERENCES

[1] Redmon J, Farhadi A. Yolov3: An incremental improvement[J]. arXiv preprint arXiv:1804.02767, 2018.
[2] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 580-587.
[3] Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[J]. Advances in neural information processing systems, 2015, 28.
[4] Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J]. arXiv preprint arXiv:2004.10934, 2020.
[5] Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector[C]//European conference on computer vision. Cham: Springer International Publishing, 2016: 21-37.
[6] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788.
[7] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 7263-7271.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 42205
Visits: 866243

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.