Authors : Tushar Dhake, Dr. V. D. Chaudhari, Dr. H. T. Ingale, H.V. Dhande, M. N. Patil
DOI : 10.46335/IJIES.2025.10.7.7
Abstract – Real-time object detection is essential for applications such as surveillance, robotics, and autonomous systems. This paper explores the implementation of edge processing on Raspberry Pi 5, leveraging its enhanced computational power alongside optimized OpenCV and YOLO algorithms. Unlike traditional centralized processing, edge computing enables faster detection with reduced latency and network dependency. We analyze system architecture, model optimization, and performance metrics to demonstrate how real-time image processing at the edge improves accuracy and efficiency. Our findings highlight that Raspberry Pi 5, combined with advanced AI models, offers a cost-effective and scalable solution for real-time edge-based object detection.
