The blend of Artificial intelligence (AI) and the Internet of Things (IoT) are opening new business frontiers. With technological achievements such as the Raspberry Pi computing models, we are witnessing significant expansion in AI potential for businesses. From automated video analytics to AI-infused assistants, AI development with Raspberry Pi 4 is heralding breakthrough applications across verticles. Let’s deeply analyze how Raspberry Pi 4 is providing for a great catalyst for artificial intelligence services to build dynamic applications.
Unlocking the Features of Raspberry Pi 4
The release of the Raspberry Pi series of single-board computers has completely revolutionized the software development space. After the remarkable performance of Raspberry Pi Zero and its successors, Raspberry Pi 4 has hit the market with improved architecture.
The Raspberry Pi 4 Model B is the latest version in the cost-effective Raspberry Pi mini-computer series.
The Raspberry Pi 4 SoC witnessed an upgrade from Cortex A53 chips to a Cortex A 72 SoC which is a major performance booster supported by a USB 3.0 port. The system is powered by 4 GB RAM with dual 4k display output at 30fps, making it a strong replacement of a viable home theatre setting. With a pre-installed Linux-based OS called NOOBS, Pi 4 is poised to accelerate the development of the following applications-
a) Video streaming and creation of stop motion movies
b) Smart Surveillance models
c) Home Automation systems
d) Retro Gaming machines, and more
However, the most disruptive applications of Raspberry Pi 4 comes with machine learning. From chatbots to object detection, AI development with Raspberry Pi 4 expands the horizons of machine learning development services across businesses. Let’s explore some of these.
IoT Applications and AI Development with Raspberry Pi 4
1) In-Depth Image and Video Analytics
Advancements in machine learning algorithms have propelled the development of deep image processing and video analytics applications. AI’s computer vision technologies are breaking new grounds with facial recognition, object detection, and real-time video surveillance models.
Though previous Raspberry Pi series ran cloud-based image processing models, Pi 4’s 64-bit quad-core processor can also train AI models at the edge efficiently. However, in the face of memory and compute restraints, it is advisable to use a cloud’s computational powers to deploy ML models such as-
a) Image Classification
Assigning specific labels to the image or video frame. The application requires coding the Raspberry Pi’s camera to capture images and record videos. Image labeling is emerging as an effective automation tool to monitor workplace activities, manufacturing cycles, and construction sites.
Learn more: AI development with Raspberry Pi 4
