A Guide to TinyML and Edge AI for Vision Applications in 2025

I've spoken with countless operations managers who have a clear goal: they want to "see" what's happening in their remote operations. They want to automatically detect defects on a conveyor belt, count the number of people entering a secured area, or read license plates in a parking facility. For years, the only option was to stream massive amounts of video data to the cloud for analysis. This approach was slow, expensive, and often unreliable.

But what if the device on-site could not only see, but understand what it was seeing in real-time? This is the revolutionary power of TinyML and Edge AI for vision. By deploying AI models directly on hardware at the edge, we can perform complex image recognition and object detection with millisecond-level latency. Let's be clear: this technology is moving from the lab to the factory floor, and choosing the right hardware is the key to a successful deployment. This is a core application for any modern  Industrial IoT Edge Gateway.

While often used together, these terms refer to two different scales of edge intelligence.

• TinyML: This refers to running highly optimized, lightweight machine learning models on extremely resource-constrained devices like microcontrollers (MCUs). Think of a simple "wake word" detection on a smart speaker. For vision, this might be a very basic presence detection.
• Edge AI: This refers to running more complex and powerful AI models on more capable edge devices, like an industrial gateway with a microprocessor (CPU) and a dedicated AI accelerator (NPU).

For any serious industrial machine vision application—like analyzing a high-resolution video stream—you are firmly in the world of Edge AI. You need a device with the processing power to handle the workload.

To run Edge AI for computer vision, a standard CPU is not enough. You need a device with a Neural Processing Unit (NPU), a specialized processor designed to accelerate the mathematical operations used in AI models.

The  Robustel EG5120 is purpose-built for these tasks.

• Powerful NXP i.MX 8M Plus Processor: The EG5120 is powered by this quad-core ARM processor, but its real secret weapon is the integrated 2.3 TOPS NPU. This allows it to perform trillions of AI calculations per second, with incredible energy efficiency.
• Open Debian-Based OS: It runs RobustOS Pro, a hardened OS based on Debian 11. This provides a stable and familiar Linux environment, allowing you to easily install AI frameworks like TensorFlow Lite, PyTorch, and ONNX.
• High-Speed Connectivity & Industrial I/O: With 5G/4G cellular, Gigabit Ethernet, and industrial interfaces like RS485 and DI/DO, it can both receive video data from cameras and trigger physical actions (like activating a robotic arm to remove a defective product).

Let's make this tangible. Imagine a bottling plant where you need to ensure every bottle has a cap.

1. The Setup: A high-resolution IP camera is positioned over the conveyor belt and connected to the EG5120's Ethernet port.
2. The Model: A lightweight object detection model, trained to recognize "bottle with cap" and "bottle without cap," is deployed as a Docker container on the EG5120 using a framework like TensorFlow Lite.
3. Local Processing: The EG5120 captures the video stream and uses its NPU to run the model on every single frame, in real-time. The real 'aha!' moment is realizing this complex analysis happens in milliseconds, right there on the factory floor.
4. The Action: If the model detects a "bottle without cap," the EG5120 instantly sends a signal via its Digital Output (DO) to a pneumatic kicker, which removes the faulty bottle from the line.
5. The Data: Instead of sending the entire video stream to the cloud, the EG5120 sends only a tiny MQTT message, like {"defect_count": 1}, for logging and analysis.

This entire process is a perfect example of Edge AI for vision: it's fast, reliable, and incredibly data-efficient.

The world of TinyML and Edge AI for vision is unlocking unprecedented capabilities for industrial automation and smart infrastructure. While TinyML is perfect for simple, low-power tasks, true industrial machine vision requires the power of Edge AI. A successful deployment depends on choosing hardware that is not only powerful but also rugged, secure, and easy to manage. By leveraging an edge gateway with an NPU like the Robustel EG5120, you get a production-ready platform that provides the processing power, software flexibility, and industrial reliability needed to transform your computer vision projects from a proof-of-concept into a scalable, real-world solution.