Skip to main navigation menu Skip to main content Skip to site footer
×
Español (España) | English
Editorial
Home
Indexing
Original

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

By
Patakamudi Swathi ,
Patakamudi Swathi

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India.

Search this author on:

PubMed | Google Scholar
Dara Sai Tejaswi ,
Dara Sai Tejaswi

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India.

Search this author on:

PubMed | Google Scholar
Mohammad Amanulla Khan ,
Mohammad Amanulla Khan

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India

Search this author on:

PubMed | Google Scholar
Miriyala Saishree ,
Miriyala Saishree

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India

Search this author on:

PubMed | Google Scholar
Venu Babu Rachapudi ,
Venu Babu Rachapudi

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India

Search this author on:

PubMed | Google Scholar
Dinesh Kumar Anguraj ,
Dinesh Kumar Anguraj

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India

Search this author on:

PubMed | Google Scholar

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

How to Cite

1.
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 May 24];3:295. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/295

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

Article metrics

Google scholar: See link

Metrics

Metrics Loading ...

The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.