Machine vision technology enables industrial equipment to “observe” and make rapid decisions based on visual data. Common applications include defect detection, guidance, dimensional measurement and identification. As one of the foundational technologies of industrial automation, it has long been instrumental in improving product quality, accelerating
production and optimizing manufacturing and logistics processes. Today, this mature technology is integrating with artificial intelligence (AI) and spearheading the transition to Industry 4.0.
Machine vision predates the application of AI, relying on embedded systems and algorithms to process image information and identify basic features. Traditional machine vision applications were relatively simple and required no AI, provided the image data was clear and easily distinguishable. Examples include barcodes and predictable shapes with precise patterns.
With the addition of edge computing and deep learning models, the capabilities and applications of machine vision are rapidly expanding. AI enhances machine vision, extending its role beyond quality control to tasks such as aiding workers in factories, warehouses and transportation systems, allowing them to focus on more value-added tasks.
Increasing adoption across various domains where machine vision systems play a critical role in production, inspection, quality control and process optimization.
Expanding usage and enhanced analysis accuracy through AI and deep learning algorithms.
Frequent use in ensuring manufacturing accuracy and defect detection.
Applications are growing in agriculture, healthcare, transportation, defense and aerospace.
Emphasis on product innovation and differentiation, with vendors expanding ecosystems through mergers, joint ventures and partnerships.
Given the rapid development and expanding application fields, the global
machine vision market is expected to surpass $30 billion by 2035. The Asia-Pacific region is projected to dominate due to its dense manufacturing infrastructure, with over half of the market share attributed to fully structured machine vision systems used in process control and quality inspection.
Machine vision’s vertical markets include manufacturing, healthcare, logistics and warehousing, transportation, agriculture, defense and aerospace. These industries benefit from efficiency and quality improvements, particularly in manufacturing where defect detection, identification, guidance and dimensional measurement are the most common applications.
Identifying flaws or quality issues using high-resolution industrial cameras and algorithms to compare real-time production with predefined standards.
Recognizing and verifying objects, characters or patterns using Optical Character Recognition (OCR) and barcode scanning.
Assisting robotic systems in performing precise tasks such as assembly or welding, using 3D cameras and motion control.
Ensuring products meet specified tolerances through accurate measurement of size, shape and geometry using high-resolution imaging and algorithms.
The integration of AI improves accuracy and expands the system’s capabilities.
Acquiring image data through industrial cameras and lights.
Using image processing to extract key features for subsequent recognition.
Applying algorithms to identify objects, text and other elements.
Analyzing and acting on the recognition results.
The technical architecture of machine vision is a comprehensive system configuration comprising multiple interconnected key components. The primary components include lights, industrial cameras and software. Additionally, components such as motion control, frame grabber and AI accelerators can be incorporated based on specific application requirements. Each of these components plays a crucial role throughout the process from image capture to decision-making and output.
At the core of the architecture is the industrial PC (IPC) or embedded system which serves as the main control center of the machine vision system. It is responsible for managing and processing overall operations.
The industrial cameras and lights are critical for image acquisition. They are typically connected to the industrial PC via RJ45 or USB interfaces, with their primary function being the capture of high-quality image data.
A proper light is essential for maximizing contrast and ensuring high-quality image capture. Poor lighting conditions cannot be compensated for even with advanced cameras and software. Common types of light include:
Positioned on the same side as the camera, typically using ring lights, suitable for uniform illumination.
Placed opposite the camera, ideal for measuring object dimensions by creating sharp contrasts.
Provides highly directional illumination, highlighting textures and creating shadows for better surface analysis.
Delivers soft, even illumination to
eliminate shadows and suppress surface textures.
The core of a machine vision system, industrial cameras are categorized by image capture methods：
Use pixel arrays to capture an entire image at once, suitable for static or slow-moving objects.
Pros： Easy to use, cost-effective, wide range of applications.
Capture one pixel row at a time, reconstructing the complete image in software. Ideal for high-speed conveyor systems.
Pros： Compact size, supports high-speed imaging.
All-in-one systems integrating cameras, algorithms and software for standalone operation.
Detect infrared light for unique applications such as low-light environments, smoke detection and thermal analysis.
Capture depth information using techniques like Time of Flight (ToF) or structured light for applications in robotics and automated inspection.
Analyze materials based on their spectral characteristics, commonly used in agriculture and material analysis.
A proper light is essential for maximizing contrast and ensuring high-quality image capture. Poor lighting conditions cannot be compensated for even with advanced cameras and software. Common types of light include:
Positioned on the same side as the camera, typically using ring lights, suitable for uniform illumination.
Placed opposite the camera, ideal for measuring object dimensions by creating sharp contrasts.
Provides highly directional illumination, highlighting textures and creating shadows for better surface analysis.
Delivers soft, even illumination to
eliminate shadows and suppress surface textures.
The core of a machine vision system, industrial cameras are categorized by image capture methods：
Use pixel arrays to capture an entire image at once, suitable for static or slow-moving objects.
Pros： Easy to use, cost-effective, wide range of applications.
Capture one pixel row at a time, reconstructing the complete image in software. Ideal for high-speed conveyor systems.
Pros： Compact size, supports high-speed imaging.
All-in-one systems integrating cameras, algorithms and software for standalone operation.
Detect infrared light for unique applications such as low-light environments, smoke detection and thermal analysis.
Capture depth information using techniques like Time of Flight (ToF) or structured light for applications in robotics and automated inspection.
Analyze materials based on their spectral characteristics, commonly used in agriculture and material analysis.
Software processes the image data captured by industrial cameras, converts image formats and extracts critical features for recognition and analysis.
Based on the extracted image features, application software performs analysis and judgment tasks, such as:
Software facilitates robotic applications. Robots act based on decisions made by the application software. For instance, robotic arms can pick semi-finished products deemed defective by the software from the production line and place them in the defective product area.
Software controls hardware devices to execute image processing and analysis. Based on the results, it makes accurate decisions, ultimately enabling automated applications.
AI accelerators are hardware devices in machine vision systems responsible for running AI models. Their primary purpose is to enhance the efficiency and accuracy of image recognition and data analysis. In scenarios involving large datasets or complex computations, AI accelerators become critical components of machine vision systems, particularly for tasks such as image recognition and analysis.
Designed specifically for graphic computations, GPU cards are powerful hardware suitable for AI tasks such as deep learning training, image generation, and natural language processing. Their high computational capacity and fast data transfer speeds make them ideal for handling extensive and complex calculations.
A complete modular system, GPU SOMs are single embedded modules that can be inserted into a carrier board to expand the system’s AI computational capabilities.
NPUs are specialized processors designed to accelerate AI applications. They mimic human neural systems and are energy-efficient, enabling long-term usage. NPUs are suitable for continuous AI computation tasks such as image generation and facial recognition.
Developed by Google, TPUs are processors designed to accelerate machine learning tasks, particularly for large-scale, low-precision computations.
VPUs are microprocessors tailored for accelerating vision-related tasks. They specialize in executing convolutional neural network (CNN) operations, focusing on single-purpose tasks.
ASICs are chips designed for specific and specialized applications, such as cryptocurrency mining, algorithm-based data analysis and inference tasks. These chips are optimized for single-use cases and are not versatile.
FPGAs are programmable logic gate arrays that can be configured to perform complex functions or act as logic gates. They offer flexibility and adaptability, along with low latency and power consumption, making them suitable for diverse edge AI applications.
DPUs are designed for network, data security and AI data management tasks. They are ideal for complex data processing tasks in data centers, such as big data, artificial intelligence, machine learning and deep learning.
AI accelerators are hardware devices in machine vision systems responsible for running AI models. Their primary purpose is to enhance the efficiency and accuracy of image recognition and data analysis. In scenarios involving large datasets or complex computations, AI accelerators become critical components of machine vision systems, particularly for tasks such as image recognition and analysis.
Designed specifically for graphic computations, GPU cards are powerful hardware suitable for AI tasks such as deep learning training, image generation, and natural language processing. Their high computational capacity and fast data transfer speeds make them ideal for handling extensive and complex calculations.
A complete modular system, GPU SOMs are single embedded modules that can be inserted into a carrier board to expand the system’s AI computational capabilities.
NPUs are specialized processors designed to accelerate AI applications. They mimic human neural systems and are energy-efficient, enabling long-term usage. NPUs are suitable for continuous AI computation tasks such as image generation and facial recognition.
Developed by Google, TPUs are processors designed to accelerate machine learning tasks, particularly for large-scale, low-precision computations.
VPUs are microprocessors tailored for accelerating vision-related tasks. They specialize in executing convolutional neural network (CNN) operations, focusing on single-purpose tasks.
ASICs are chips designed for specific and specialized applications, such as cryptocurrency mining, algorithm-based data analysis and inference tasks. These chips are optimized for single-use cases and are not versatile.
FPGAs are programmable logic gate arrays that can be configured to perform complex functions or act as logic gates. They offer flexibility and adaptability, along with low latency and power consumption, making them suitable for diverse edge AI applications.
DPUs are designed for network, data security and AI data management tasks. They are ideal for complex data processing tasks in data centers, such as big data, artificial intelligence, machine learning and deep learning.
The applications of machine vision can be categorized into defect detection, guidance, dimensional measurement and identification. Its technical principles consist of steps such as image input, image processing and feature extraction, object recognition and evaluation, decision-making and output.
The primary technical architecture is composed of lights, industrial cameras and software. Depending on the application scenario, additional components such as motion control, frame grabber and AI accelerators can be included. An industrial PC or embedded system serves as the control center of the machine vision system, managing and processing operations.
