Fusion of Deep Learning and Multi View Geometry for Robust Object Detection in Distributed Camera

Abstract

Object detection is a cornerstone of modern technologies, playing a pivotal role in applications such as autonomous driving, robotics, industrial automation, and the development of smart environments. The demand for robust and accurate detection systems has never been more critical, as even minor inaccuracies can lead to significant challenges, particularly in safety-critical domains like automated driving where human lives are at stake. Addressing these challenges, this study introduces a novel approach that integrates multi-view geometry with deep learning techniques to develop a system capable of achieving superior object detection accuracy and reliability.

The proposed system is built around a custom-trained YOLOv5 model, meticulously designed to enhance performance and achieve dimension estimation with an impressive margin of error within 2%. By utilizing multiple camera inputs, the system demonstrates substantial improvements over traditional single-camera setups in both detection robustness and spatial accuracy. The advantages of this multi-camera, geometry-aware approach are offering greater precision and consistency across diverse scenarios. This breakthrough has the potential to revolutionize multiple industries, enabling safer autonomous systems, more reliable security solutions, efficient industrial manufacturing processes, advanced robotics capabilities, and the creation of intelligent, adaptive environments.