Enhancing Real-Time Object Detection in Autonomous Systems Using Deep Learning and Computer Vision Techniques

Abstract

This study explores advancements in real-time object detection within autonomous systems using deep learning and computer vision techniques. Focusing on the unique challenges that autonomous systems face in dynamic environments, this research employs a qualitative approach to assess how recent developments in convolutional neural networks (CNNs) and machine learning algorithms contribute to enhanced detection accuracy and processing speed. Data were collected through expert interviews, in-depth literature analysis, and case studies examining real-world applications in autonomous vehicles, drones, and robotics. The findings reveal that integrating advanced deep learning frameworks, such as YOLO and Faster R-CNN, with optimized computer vision processing significantly improves object recognition capabilities, even in complex scenarios with high object density or varying lighting conditions. Furthermore, this study identifies current limitations in hardware dependency and computational intensity, underscoring the importance of resource-efficient models for real-time performance. The insights gained offer valuable implications for developers and researchers aiming to refine object detection systems in autonomous technologies. Future research should consider hybrid approaches that combine deep learning with traditional computer vision techniques to further enhance performance in real-time applications. This research highlights the transformative potential of AI-driven methodologies for making autonomous systems safer and more reliable in real-world operations.