MMoNet3D: Enhancing monocular 3D object localization with modern advancements
Department of Information Technology, Vasireddy Venkatadri Institute of Technology, Nambur, Guntur, Andhra Pradesh, India.
Research Article
World Journal of Advanced Research and Reviews, 2024, 21(03), 1943–1952
Article DOI: 10.30574/wjarr.2024.21.3.0906
Publication history:
Received on 14 January 2024; revised on 20 March 2024; accepted on 22 March 2024
Abstract:
MMoNet3D, an innovative project, addresses monocular multi-object detection and 3D localization in real-time, surpassing the limitations of the existing MoNet3D system. Utilizing a custom deep learning model inspired by Centernet, it excels in oriented car detection across static images, videos, and live webcam feeds. The system, powered by the KITTI dataset, showcases its potential impact on real-time monocular multi-object detection. Its unique features, including 3D bounding box visualization and a user-friendly GUI, set MMoNet3D apart, making it suitable for embedded advanced driving-assistance systems. This advancement promises heightened accuracy and efficiency, contributing to enhanced vehicular safety and operational efficacy. MMoNet3D stands as a beacon in computer vision, paving the way for intelligent transportation systems.
Keywords:
Monocular Multi-Object Detection; 3D Object Localization; Real-time Computer Vision; Custom Deep Learning Model; Centernet Inspired Architecture; Live Webcam Feed Processing; Advanced Driving-Assistance Systems (ADAS); KITTI Dataset Integration
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0