Real-Time Vehicle Detection for Traffic Monitoring: A  Deep Learning Approach

Patakamudi Swathi; Dara Sai Tejaswi; Mohammad Amanulla Khan; Miriyala Saishree; Venu Babu Rachapudi; Dinesh Kumar Anguraj

doi:10.56294/dm2024295

Original

Published: 2024-04-13

DOI: https://doi.org/10.56294/dm2024295

Real-Time Vehicle Detection for Traffic Monitoring: A Deep Learning Approach

Abstract

Vehicle detection is an essential technology for intelligent transportation systems and autonomous vehicles. Reliable real-time detection allows for traffic monitoring, safety enhancements and navigation aids. However, vehicle detection is a challenging computer vision task, especially in complex urban settings. Traditional methods using hand-crafted features like HAAR cascades have limitations. Recent deep learning advances have enabled convolutional neural networks (CNNs) like Faster R-CNN, SSD and YOLO to be applied to vehicle detection with significantly improved accuracy. But each technique has tradeoffs between precision and processing speed. Two-stage detectors like Faster R-CNN are highly accurate but slow at 7 FPS. Single-shot detectors like SSD are faster at 22 FPS but less precise. YOLO is extremely fast at 45 FPS but has lower accuracy. This paper reviews prominent deep learning vehicle detectors. It proposes a new integrated method combining YOLOv3 detection, optical flow tracking and trajectory analysis to enhance both accuracy and speed. Results on highway and urban datasets show improved precision, recall and F1 scores compared to YOLOv3 alone. Optical flow helps filter noise and recover missed detections. Trajectory analysis enables consistent object IDs across frames. Compared to other CNN models, the proposed technique achieves a better balance of real-time performance and accuracy. Occlusion handling and small object detection remain open challenges. In summary, deep learning has enabled major progress but enhancements in model architecture, training data and occlusion handling are needed to realize the full potential for traffic management applications. The integrated method proposed offers improved performance over baseline detectors. We have achieved 99 % accuracy in our project

Keywords:

Multi Detecting Object Tracking,

Convolution Neural Network,

Deep Learning,

Traffic Detection,

Machine Learning,

Image Classification,

How to Cite

Swathi P, Sai Tejaswi D, Amanulla Khan M, Saishree M, Babu Rachapudi V, Kumar Anguraj D. Real-Time Vehicle Detection for Traffic Monitoring: A Deep Learning Approach. Data and Metadata [Internet]. 2024 Apr. 13 [cited 2024 Jul. 27];3:295. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/295

Copyright Notice

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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