Real-Time Image Processing for Autonomous Systems using Lightweight Deep Neural Networks

Abstract

In recent years, the demand for real-time image processing in autonomous systems has increased significantly, driven by advancements in artificial intelligence and computer vision. Traditional deep neural networks, while highly accurate, often require substantial computational resources, making them unsuitable for real-time applications in embedded and edge devices. To address this challenge, this research introduces a Fast-SCNN-based lightweight deep learning framework designed for efficient and real-time image processing in autonomous systems. The model performs simultaneous image segmentation and scene understanding with minimal latency, ensuring rapid decision-making for autonomous navigation and environmental perception. The proposed Fast-SCNN architecture leverages a dual-branch structure combining feature learning and spatial detail preservation to achieve both speed and accuracy. By optimizing convolutional operations and employing depth wise separable convolutions, the model significantly reduces computational complexity without compromising performance. Experimental evaluations demonstrate that the proposed framework achieves high accuracy and low inference time on benchmark datasets, making it suitable for real-time applications such as autonomous vehicles, drones, and robotic vision systems. This study highlights the potential of lightweight deep learning models to enable sustainable, high-performance, and energy-efficient autonomous systems.