Intelligent Embedded Vision Systems for Autonomous Machines

Abstract

Intelligent embedded vision systems have emerged as a fundamental enabling technology for autonomous machines operating in industrial automation, autonomous vehicles, service robotics, agricultural robotics, healthcare systems, and smart surveillance. Unlike conventional machine vision systems that rely on centralized cloud processing, embedded vision integrates cameras, processors, artificial intelligence (AI), and edge computing into compact hardware platforms capable of performing real-time perception and decision-making. Now, the use of new advancements, such as deep learning, computer vision, edge AI accelerators and low-power embedded processors have vastly improved object detection accuracy, scene understanding and autonomous navigation at lower latencies and energy consumption. However, the embedded vision systems still have problems with locked in computational limitation, environmental variations, reliability of other sensors as well as cybersecurity and explainability of AI decisions. Abstract: This study provides a comprehensive overview and a conceptual framework for intelligent embedded vision systems of autonomous machines. As such, our architecture combines several components that include embedded cameras, AI on edge, sensor fusion, deep neural networks and adaptive control to optimize perception and operational intelligence. The results show that the proposed framework is comparably better than classical embedded vision architectures in terms of object recognition, inference time, navigation accuracy and energy consumption. This paper also pinpoints gaps and future opportunities with respect to current research including explainable AI, neuromorphic computer, federated learning and digital twin technologies. The results provide researchers and practitioners with insights into designing the next-generation autonomous robotic platforms to be ready for Industry 5.0 environment.