A Smart Traffic Volume Measurement Based on Deep Learning

International Journal of Emerging Research in Science, Engineering, and Management

Vol. 2, Issue 1, pp. 318–324, January 2026

https://doi.org/10.58482/ijersem.v2i1.43

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

A Smart Traffic Volume Measurement Based on Deep Learning

1B Hima Bindhu, 1M Nandini, 1T Navaneetha, 1B Kedharinath Reddy, 1VS Zuberuddin Ali, 2S Dhanush

1Department of CSE, Siddharth Institute of Engineering & Technology, Puttur, India.
2Professor & Head, Department of Mechanical Engineering, Siddartha Institute of Science and Technology, Puttur, India.

Abstract

The detection and monitoring of autonomous vehicles are essential components of intelligent transportation management and control systems. By using the latest advancements in machine learning and deep learning, computers can detect, classify, and track multiple objects from captured images or videos. This paper uses the AdaBoost algorithm to create an intelligent, reliable, and efficient vehicle detection and tracking system based on aerial images. Also, the segmentation techniques can be used to separate the targeted objects from other background elements. Finally, the paper is useful for detecting the traffic density, identifying the vehicles in traffic, and evaluating the traffic flow conditions on the road. The AdaBoost algorithm for traffic volume measurement presents a promising approach towards efficient and accurate traffic analysis. Its adaptability to various environmental conditions and scalability makes it well-suited for real-world applications in traffic management and urban planning. Based on experimental analysis, the proposed Ada Boost algorithm achieved a 95% accuracy in estimating the traffic density on the road.

Keywords: Convolutional Neural Network, Computer Vision, Deep Learning, Vehicle Counting, Traffic Volume.

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DOI: https://doi.org/10.58482/ijersem.v2i1.43

Open Access • Peer Reviewed Article

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This article is part of Volume 2, Issue 1 (January 2026)

2026-01-31