International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 08 | Aug 2024
p-ISSN: 2395-0072
www.irjet.net
Deep Learning in Computer Vision: From Object Detection to Automobile Vehicles Joseph Jeremiah Adekunle1, Adebanwo Hassan Adebola2, Ntekim Emmanuel Effiong3 Mahmud Mahmud Lawal4 1Department of Computer Science, National Open University of Nigeria
.2Department of Electrical and Electronic Engineering, Federal University of Petroleum Resources Effurun 3Automotive Mechatronics Management Department, University of Applied Sciences Upper Austria 4 Department of Electrical Engineering Ahmadu Bello University Zaria, Nigeria ---------------------------------------------------------------------------***--------------------------------------------------------------------------Abstract - Deep learning has significantly advanced the field of computer vision, transforming how machines interpret
visual data and enabling new applications such as autonomous vehicles. This article reviews the development of deep learning technologies, from early pattern recognition to sophisticated Convolutional Neural Networks (CNNs). It examines the role of deep learning in improving object detection accuracy and real-time performance, which is crucial for the safe operation of autonomous vehicles. The discussion addresses technical challenges including data scarcity, high computational costs, and the need for large-scale datasets. Ethical concerns such as privacy issues and potential bias in AI models are also explored. The article concludes by considering future directions, including advancements in deep learning models and their integration with other AI technologies. It highlights the potential for deep learning to revolutionize transportation and underscores the importance of collaboration between tech companies, automakers, and regulators to address the challenges and ensure the responsible deployment of autonomous vehicles.
KEYWORDS: Deep Learning, Computer Vision, Object detection, Autonomous Vehicles
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