边缘学习的工作原理

悬挂在传送带上方的康耐视视觉系统检测金属铸件

通过使用基于智能相机的单一解决方案,边缘学习可以在几分钟内部署到任何工厂线上。这种解决方案集成了多个组件,包括机器视觉硬件、基于规则的工具和人工智能 (AI) 功能。

The In-Sight 2800 vision system, embedded with edge learning technology, facing left

Hardware

Edge learning packs sophisticated hardware into a small form factor. It runs entirely in a smart camera equipped with integrated lighting, an autofocus lens, and a powerful sensor. All of these hardware features make edge learning possible.

Lighting is key for a high-quality imaging as it maximizes contrast, minimizes dark areas, and brings out the necessary detail.

A high-speed autofocus lens ensures that the object of interest is always in focus, even as distance changes. It does so by instantly adjusting focus as the region of interest (ROI) changes. Liquid autofocus lenses are smaller and lighter than equivalent mechanical lenses, reducing the size and weight of the camera while making it resistant to the shock and vibration of a production line.

A large and capable sensor offers high resolution and a wide field of view (FOV) for any given application.

Machine Vision Tools

Rule-based vision tools are well-suited for specialized tasks, such as location, measurement, and orientation. For the purposes of edge learning, they are combined in ways specific to the demands of factory automation, eliminating the need to chain vision tools or build complex logic sequences when training the system.

These tools provide fast preprocessing of images, extracting density, edge, and other feature information for the purposes of detecting and analyzing manufacturing defects. By identifying and clarifying the relevant parts of the image, these tools reduce computational load, compared to traditional deep learning approaches.

 Icons representing rule-based tools such as measure distance, pixel count and math and logic tools
Abstract image of human head with nodes and gears representing artificial intelligence

AI Capabilities

Instead of using rules created by human programmers, AI learns by example, building a neural network and devising effective pass/fail thresholds from labeled examples of acceptable and unacceptable parts. In essence, it mimics the way humans learn.

AI capabilities can have large training requirements. Edge learning, on the other hand, takes advantage of the fact that factory automation images have specific structural contents, and so pre-trains its algorithms with that domain knowledge. Not starting from scratch results in a less programming-intensive application.

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