Machine vision applications have a wide range of classifications and applications, and can be categorized according to different criteria. Below are some common classification methods and their application scopes:
Classified by application area:
1. Industrial Automation
●Quality inspection: Check the appearance defects, dimensional accuracy, etc. of the product.
● Robot guidance: Guiding robots to perform tasks such as material handling and assembly.
● Barcode recognition: Reading product barcodes for tracking and management.
2. Medical field
●Medical imaging: such as CT and MRI image processing.
●Pathological analysis: Automatically analyzes images of pathological sections under a microscope.
● Ophthalmic diagnosis: such as the analysis and diagnosis of retinal images.
3. Automotive Industry
● Autonomous driving: Recognizing road signs, pedestrians, vehicles, etc.
●Production Inspection: Inspecting the quality and assembly of automotive parts.
4. Security monitoring
● Facial recognition: Used for identity verification and security monitoring.
●Behavioral analysis: Monitor and analyze personnel behavior to identify potential threats.
5. Agriculture
●Crop monitoring: Monitor the growth status and health level of crops.
●Machine harvesting: Guide robots to harvest fruits and vegetables.
6. Retail
●Customer flow analysis: Analyze in-store customer flow to optimize displays and layout.
●Automatic checkout: The system automatically completes the checkout process by recognizing products through images.
Classified by technology:
1.2D vision
● It uses a regular camera to capture two-dimensional images, suitable for most basic detection and recognition tasks.
2.3D vision
● It uses technologies such as lasers, structured light, or stereo vision to capture 3D images, suitable for applications requiring precise measurement and complex shape analysis.
3. Thermal imaging
● Infrared cameras are used to capture thermal radiation images of objects, and are widely used in fields such as nighttime surveillance and equipment temperature detection.
4. Spectral Imaging
●Analyze spectral data at different wavelengths for applications in food safety testing, agricultural monitoring, and other fields.
Classified by processing method:
1. Based on traditional algorithms
● Edge detection, feature extraction, pattern matching, etc.
● Use deep learning models such as convolutional neural networks (CNN) to perform complex tasks such as image classification, object detection, and semantic segmentation.
Machine vision technology has a wide range of applications, covering everything from industrial manufacturing to daily life. In the future, with the advancement of technology, its application areas will become even broader and deeper.
