Traffic Signal Violation Detection System

Authors

  • K. Pujitha B. Tech Student, Department of Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh, India Author
  • J. Indu B. Tech Student, Department of Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh, India Author
  • B. Sasi Vardhan B. Tech Student, Department of Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh, India Author
  • P. Sandeep Kumar B. Tech Student, Department of Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh, India Author
  • Mrs. G. Ramadevi Assistant Professor, Department of Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh, India Author

DOI:

https://doi.org/10.32628/CSEIT2511141

Keywords:

Deep Learning, Object Detection, SSD MobileNet V1, Real-time Traffic Monitoring, CNN

Abstract

Traffic signal violations are a major cause of accidents and traffic congestion. This project presents an automated Traffic Signal Violation Detection System using Deep Learning-based Object Detection. The system leverages SSD MobileNet V1, a pre-trained Convolutional Neural Network (CNN), to detect and classify traffic signals in real-time. Using the TensorFlow Object Detection API, the model identifies traffic lights and determines violations based on detected signals. The approach integrates image processing, real-time object detection, and violation recognition, providing an intelligent traffic monitoring solution. The proposed system enhances road safety, reduces human intervention, and supports smart city traffic management systems.

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References

Mochamad Mobed Bachtiar, Achmad Rahman Mawardi, Adnan Rachmat Anom Besari. Vehicle Classification and Violation Detection on Traffic Light Area using BLOB and Mean- Shift Tracking Method, 2020 International Conference on Applied Science and Technology (iCAST), October 2020

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B. C. R, S. Joy, U. A. Reddy, R. C. Lal, V. R and A. D. M, "Traffic Rule Violation Recognition for Two Wheeler using YOLO Algorithm," 2023 Second International Conference on Electronics and Renewable Systems (ICEARS), Tuticorin, India, 2023, pp. 1477-1480, doi: 10.1109/ICEARS56392.2023.10085537.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

10-02-2025

Issue

Vol. 11 No. 1 (2025): January-February

Section

Research Articles

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Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology

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