Object Detection with Raspberry Pi 5

This article discusses the Raspberry Pi 5 8GB, a powerful single-board computer suitable for various applications, including automation systems and AI-powered object recognition. It highlights the Pi 5's enhanced CPU and GPU performance, making it a viable option for tasks such as running multiple applications and handling data-intensive processes. The article also covers the use of the Pi 5 in AI object detection with Yolov8, noting that while it can perform these tasks, its processing speed is limited to about 2 frames per second, which may not meet the needs of real-time applications. Overall, the Raspberry Pi 5 is presented as a versatile, compact, and energy-efficient platform ideal for semi-experienced makers looking to explore AI and automation projects.

By : Jonathan Chua

Product and Application Details

Product:

Get Yours now🛒 : Raspberry Pi 5 - 8GB

The Raspberry Pi 5 8GB is a single-board computer (SBC) that falls under the category of microcomputers. It is designed for educational, hobbyist, and industrial applications, offering a low-cost platform for programming, electronics projects, and computing tasks. The "8GB" refers to its RAM capacity, making it suitable for more demanding tasks, such as running multiple applications, servers, or handling data-intensive tasks. In comparison with its predecessor the Raspberry Pi 4, it delivers a 2-3x increase in CPU performance, and a significant uplift in GPU performance, alongside improvements to camera, display, and USB interfacing.

Raspberry Pi 5

Application:

The Raspberry Pi 5’s versatility and powerful performance make it suitable for a wide range of applications:

1. Mini Portable Computer :

The Raspberry Pi 5 is a tiny, lightweight computer that can replace traditional desktops or laptops for tasks like web browsing, word processing, and coding. Its compact size (85mm by 56mm) and weight (50 grams) make it highly portable.

Normal browsing with Raspberry Pi 5

2. Automation System :

The Raspberry Pi 5 can serve as the central controller in smart home or industrial IoT systems, managing devices like smart lighting, security systems, and thermostats. Its processing power supports complex automation tasks and real-time data processing.

3. AI-Powered Object Recognition :

With its enhanced GPU, the Pi 5 can handle AI tasks such as object detection and image recognition. 

Object Detection with Raspberry Pi 5

Visual Materials

• Photos

Technical Details

Edge AI with Raspberry Pi 5:

• Object detection with Yolov8

• Main programming language: Python

• Libraries use: OpenCV-Python, Ultralytics (Yolov8) 

• An overview diagram of the software stack will be helpful

Downloading Libraries

• Other hardware components used : Monitor, Keyboard, Mouse.

• For hardware, an overview of the whole system will be helpful too.

Hardware Setup

User Experience

• Key Features:

  • The enhanced computational power of the Raspberry Pi 5, especially its upgraded GPU, significantly boosts its ability to handle tasks that require high processing power, such as AI-driven object detection.

  • The Raspberry Pi OS, built on the Linux platform, is a major advantage, offering excellent compatibility with a wide range of existing Linux-based software. This compatibility gives users access to a vast ecosystem of applications, tools, and libraries that are often unavailable on competing platforms. 

• Benefits and Challenges:

  • The main advantages of using the Raspberry Pi 5 in this project are its relatively small size and lightweight design, making it highly portable and easy to integrate into compact setups. Additionally, the Raspberry Pi 5 is energy-efficient for its performance level, especially when compared to traditional desktop computers, which makes it an ideal choice for projects that may need to be battery-powered or operate in environments where power consumption is a concern.

  • However, despite its strengths, the Raspberry Pi 5 has limitations when it comes to real-time AI-driven object detection. While it can perform such tasks, it currently processes at a speed of only about 2 frames per second, which is insufficient for projects that require fast, real-time image processing and detection. This makes it less suitable for applications where quick response times are critical, such as in certain AI-driven robotics or security systems that rely on rapid image analysis.

Raspberry Pi Running Interface on Video

Learning Outcomes

• Target Audience:

  • Semi - experienced makers

• Project Focus:

  • Basic AI-deployment

• Expected Outcomes:

  • Learn the basic of using AI to detect objects