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.
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