Components of a machine vision system

Machine vision systems are widely used in industrial automation, intelligent monitoring, medical imaging and other fields to improve production efficiency and product quality.

Part of machine vision system

1. Camera/sensor

The camera or sensor is used to collect image data and is the "eyes" of the machine vision system. Different types of cameras (such as industrial cameras, smartphone cameras) and sensors (such as infrared sensors) can be used for different application scenarios.

2. Image processing unit

The image processing unit is responsible for the processing and analysis of the acquired image data, including image filtering, enhancement, feature extraction and other operations, and subsequent image recognition and analysis.

3. Algorithm and model

Machine learning algorithms and models play a key role in machine vision systems and are applied to tasks such as image recognition, object detection, and defect detection. Common algorithms include convolutional neural networks (CNN), support vector machines (SVM), decision trees, etc.

4. Processor/computing unit

Processors or computing units are used to execute image processing algorithms and models for real-time image analysis and recognition tasks. Commonly used processors include central processing units (cpus), graphics processing units (Gpus), and specialized neural network processors (such as Tpus).

5. User interface/output device

User interfaces and output devices are used to display processing results and typically include displays, alarm systems, or other feedback devices that enable users to view and respond to the system's output in real time.

6. Control system

The control system is used to manage the operation of the entire machine vision system, including image acquisition, processing, analysis and output, to ensure the efficient operation and accuracy of the system.

7. Storage devices

The storage device is used to save the collected image data, processing results, model parameters and other information for subsequent analysis, review, and optimization of system performance.