We'll walk you through the process of building an edge AI application that performs defect detection from scratch.
In the rapidly evolving landscape of artificial intelligence (AI), Edge AI has emerged as a game-changer, especially for manufacturing and industrial use cases. One notable application is the development of automated defect detection systems which can be used to automate and improve quality control processes. In this blog post, we will walk you through the process of creating an edge AI enabled application, specifically a defect detection solution. We'll also explore key principles and demonstrate how to scale your solution for efficiency and reliability, and demonstrate how easy it is to build an automated defect detection solution using Modzy.
Before diving into a practical example, let's discuss some essential principles that underpin a successful Edge AI implementation. These principles are technology-agnostic and can be applied to various Edge AI use cases:
1. Separate Model Logic from Application Logic: In Edge AI, it's crucial to separate the AI model's logic from the application logic. This allows you to make changes to your application without affecting the model, making maintenance and updates more manageable.
2. Protocols and Networking: Ensuring stable and reliable communication is vital. Choose the right protocols and networking strategies to keep your devices connected and data flowing seamlessly.
3. Containerization: Edge AI systems can involve multiple components, such as data connectors, models, and user interfaces. Containerization allows for efficient resource allocation and scalability, optimizing performance.
Now, let's apply these principles to an actual use case—a defect detection system for manufacturing. Imagine a scenario where you have a camera on the factory floor and a powerful NVIDIA Jetson Nano device to process the data. The goal is to use this system to detect defects in manufactured parts more efficiently and reliably than manual inspection.
Data Collection and Preprocessing: The first component is the data connector. It handles data collection and preprocessing, including capturing video streams. Whether it's video data, sensor data, audio data, or text data, understanding how to extract, format, and transmit data is crucial.
AI Model: The AI model sits at the heart of the system. In this case, we’ll use a custom YOLO model. Containerizing the model logic allows it to run independently, reducing the need for manual interaction with the edge device.
User Interface (UI): The third component is the user interface. It can be a physical interface or custom software designed to display AI model insights and predictions. This UI can be integrated into existing enterprise software, facilitating actionable insights.
By separating components and adopting containerization, you gain flexibility, scalability, and maintainability. What’s more, by using a tool like Modzy to manage AI deployment and updates, you no longer need to manually interact with edge devices for every tweak or update. Instead, you can make changes to the application independently, improving overall system efficiency.
Edge AI is transforming manufacturing quality control, and automated defect detection is just one example of its potential. By adhering to the principles of separation, network stability, and containerization, you can create scalable and efficient Edge AI systems. The ability to remotely monitor and manage multiple edge devices from a centralized location like Modzy further enhances the power of this technology. Check out the video below to see how easy it is to get started building an edge AI application with Modzy today.