Integrating HOG-Based Vehicle Detection with CNN-Based Lane Detection for Autonomous Driving

R., Karthickmanoj and S.Aasha, Nandhini and D., Lakshmi and R., Rajasree (2024) Integrating HOG-Based Vehicle Detection with CNN-Based Lane Detection for Autonomous Driving. Journal of Innovation and Technology, 2024 (09). pp. 1-6. ISSN 2805-5179

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Abstract

The advancement of autonomous driving systems hinges on accurate and reliable vehicle and lane detection. This paper presents an integrated method to improve autonomous driving systems by merging Histogram of Oriented Gradients (HOG)-based vehicle detection with Convolutional Neural Network (CNN)-based lane detection. HOG effectively identifies vehicles by capturing edge orientations and structural features, while CNNs excel in detecting intricate lane patterns through deep learning. The combination of these techniques offers a robust solution for detecting both vehicles and lanes, essential for autonomous navigation. Evaluated across a diverse dataset featuring various driving conditions, the system's performance is measured using precision, recall, F1 score (for vehicle detection), and accuracy (for lane detection). The results indicate significant enhancements in detection capabilities, leading to improved situational awareness and safer navigation. Future work will aim to refine the system further and tackle challenges in more complex driving environments, marking this approach as a promising advancement in autonomous driving technology.

Item Type:	Article
Uncontrolled Keywords:	Autonomous Vehicles, Vehicle Detection, Histogram of Oriented Gradients (HOG), Support Vector Machine (SVM)
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TL Motor vehicles. Aeronautics. Astronautics
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	16 Aug 2024 03:50
Last Modified:	16 Aug 2024 03:50
URI:	http://eprints.intimal.edu.my/id/eprint/1983

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