Deep Learning Based Novel Vehicle Detection and Counting System Using Intelligent Transportation Systems

Abstract

It proposes deep learning-based vehicle detection and counting system designed to enhance real-time traffic monitoring and urban planning. The system employs advanced object detection models, including YOLO (You Only Look Once) and Faster R-CNN, to identify and track vehicles in various environmental conditions, such as different lighting, weather, and traffic densities. Unlike traditional sensor-based methods, which are prone to inefficiencies and high maintenance costs, this approach offers a scalable and cost-effective solution with high accuracy. The core functionality of the system revolves around the integration of robust tracking mechanisms, which enable precise vehicle counting through line-crossing or region-based techniques. By tracking vehicles across multiple frames, the system ensures accurate counts, even in the presence of occlusions or overlapping vehicles.This deep learning-based system is designed to integrate seamlessly into intelligent transportation systems, smart cities, and urban planning efforts, providing real-time data for decision-making. It offers significant improvements in traffic management, addressing the limitations of traditional vehicle detection methods. With its ability to handle complex scenarios and provide real time analytics, this system plays a crucial role in optimizing traffic flow, enhancing safety, and contributing to more efficient urban transportation systems.

Introduction

The project aims to develop a real-time, deep learning-based system for vehicle detection and counting that performs reliably across diverse environmental conditions. It leverages advanced object detection models like YOLO and Faster R-CNN to accurately identify vehicles in video footage.

The system integrates vehicle detection, counting (based on vehicles crossing defined zones), and tracking to maintain consistent vehicle identities across frames. Additionally, it estimates vehicle speed using pixel-to-distance conversion and frame rate data. Collected data is analyzed and reported for further insights.

Testing shows the system performs well in normal daylight and adverse weather conditions like rain, as well as complex intersection scenarios. However, challenges remain in night-time and high-speed situations, indicating areas for future improvement to boost accuracy under low light and fast motion.

Conclusion

In conclusion,It can successfully integrate advanced deep learning techniques with real-time traffic monitoring to enhance vehicle detection, tracking, and speed estimation. The system contributes to intelligent traffic monitoring, reducing congestion and improving road safety through automated vehicle analysis.

References

[1] Y. Redmon and A. Farhadi, “YOLOv4: Optimal speed and accuracy of object detection,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 713-721. [2] X. Ren, K. He, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2020. [3] S. Bhatnagar, A. Kumar, and D. Bhatia, “Real-time vehicle detection and tracking using deep learning algorithms,” IEEE Access, vol. 8, pp. 131237-131246, 2020. [4] T. D. Pham, S. M. R. S. Taha, and H. N. Tran, “Intelligent transportation system for vehicle counting and classification using deep learning models,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 3967-3976, 2021. [5] J. Liu, H. Wang, and Z. He, “A review of object detection and tracking algorithms for smart traffic systems,” IEEE Access, vol. 9, pp. 145235-145251, 2021.

Copyright

Copyright © 2025 Bhavaneeswaran S, Santhiya G, Vidhyashri B, Prof. Geetha M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.