Convolutional neural networks for real-time object detection with raspberry Pi

With CSSNs integrated into Raspberry Pi, finding objects quickly in real-time at the edge is now possible. This article covers everything you need to know about deploying CNNs on Raspberry Pi, starting with model maintenance, through training, and ending with real-life use and improvements. MobileNet and Tiny-YOLO are the lightweight architectures we study, and they successfully provide accurate results despite facing harsh hardware rules typical in edge computing. IA is set up using the instructions in this guide, with TensorFlow and OpenCV used and optimization done by quantization and pruning. We also study how the Google Coral USB and Intel Neural Compute Stick 2 accelerators can boost performance without too much stress on the Raspberry Pi. In addition, the article shows how CNN-powered Raspberry Pi can be used effectively in smart surveillance, driverless cars, and home automation. We also look at what is new in edge AI, hardware-improved Raspberry Pi devices, and the increasing popularity of TinyML, outlining future options for using AI on a budget. By bringing powerful deep learning together with easy-to-use hardware, this article shows developers, educators, and enthusiasts how to locally create intelligent devices that respond in real-time. A result of this expertise is that users can take their AI ideas and make them work in real situations.