Overview of Machine Vision Technology
General Introduction
Machine vision is an integrated engineering technology that empowers machine systems to perform non-contact perception,
measurement, and recognition of targets through optical imaging, sensor technology, and computer algorithms. Its core
objective is to simulate and surpass human visual capabilities, enabling automated inspection, guidance, and control in
industrial production and diverse scenarios. As a quintessential interdisciplinary field, it integrates optics, mechanical
engineering, electrical engineering, computer science, and artificial intelligence, forming the critical perceptual component
of modern automated and intelligent systems.
I. Core Components of a Machine Vision System
A standard machine vision system comprises two major components—hardware and software—which collaborate to complete
the entire process from image acquisition to information output.
Hardware Subsystems
Imaging Components:
Includes industrial lenses, industrial cameras (typically CCD or CMOS sensors), and supporting lighting systems. Lighting
highlights target features and creates stable imaging conditions; lenses handle optical imaging; cameras convert optical
signals into digital image signals. This component serves as the system's “eyes.”
Computing Platform:
Typically an industrial computer or embedded processing unit, it executes image processing algorithms and
functions as the system's “brain.”
Interface and Actuator: Includes interfaces for communicating with PLCs (Programmable Logic Controllers), robots, or other
automated equipment, and ultimately triggers the action of actuators (such as robotic arms, rejectors, or alarms).
Software and Algorithm Subsystem
Image Processing Library: Provides fundamental functions including image enhancement, filtering, morphological
operations, and edge detection.
Core Analysis Algorithms: Incorporate traditional feature extraction, template matching, spot analysis, and dimensional
measurement algorithms, alongside advanced deep learning-based models for classification, detection (e.g., YOLO,
R-CNN series), and segmentation (e.g., U-Net).
Application & User Interface: Encapsulates algorithms into configurable solutions, offering parameter settings, workflow
orchestration, result visualization, and human-machine interaction interfaces.
II. Fundamental Working Principles of Machine Vision
The machine vision workflow is a sequential data processing pipeline comprising four primary stages:
Image Acquisition: Under controlled lighting conditions, an industrial camera captures light signals from a scene via shutter
exposure, converting them into a digital matrix (pixel array) to generate raw digital images.
Image Preprocessing: Optimizes raw images to enhance subsequent analysis reliability. Common operations include grayscaling,
noise suppression (e.g., Gaussian filtering), contrast enhancement, and geometric correction—aiming to improve image quality
and highlight regions of interest (ROI).
Image Analysis and Feature Extraction: This constitutes the core technical phase. The system employs algorithms to quantitatively
extract key information from preprocessed images.
* Traditional Methods: Obtain geometric features (e.g., position, dimensions, angle, area, circularity) of targets through edge
detection (e.g., Canny operator), binarization, and contour finding.
* Deep Learning Methods: Leverage pre-trained convolutional neural networks (CNNs) to automatically learn and extract deep
semantic features from images, directly outputting classification results, target bounding boxes, or pixel-level segmentation maps.
Recognition, Judgment, and Output: Compare, evaluate, or measure extracted features against preset standards or models.
Generate control commands based on results (e.g., OK/NG, coordinates, readings) and transmit them to actuators via I/O
interfaces or communication protocols (e.g., Ethernet/IP, PROFINET) to complete a work cycle.
III. Key Technical Features of Machine Vision
Non-contact Measurement: Avoids physical damage or deformation to measured objects, suitable for precision, delicate,
or hazardous items.
High Precision and Repeatability: Achieves sub-pixel measurement accuracy while maintaining consistent judgment results
under identical conditions, unaffected by subjective factors or fatigue.
High-Speed Processing Capability: Leveraging robust hardware and efficient algorithms, it completes image acquisition,
processing, and decision-making within milliseconds, meeting the demands of high-speed production lines.
Environmental Adaptability: Through specialized lighting (e.g., structured light, infrared), lens selection, and robust algorithms,
it withstands complex industrial environments including vibration, high temperatures, dust, and fluctuating lighting conditions.
Information Integration & Traceability: Beyond pass/fail determination, the system records, stores, and links images with
product data, enabling production traceability and quality analysis.
IV. Primary Application Areas
Industrial Manufacturing & Quality Inspection: The most mature application domain for machine vision. Applications include:
- Appearance defect detection (scratches, stains, assembly integrity)
- Precision dimensional measurement (part geometric tolerances)
- Guidance and positioning (robotic vision-guided grasping, placement)
- OCR/OCV (character recognition and verification)
Intelligent Transportation and Security:
Covers license plate recognition, traffic flow monitoring, driver status monitoring;
plus public safety applications like facial recognition, behavior analysis, perimeter intrusion detection.
Medical Imaging Analysis: Assists physicians in lesion identification, segmentation, and quantitative analysis of medical
images (X-rays, CT scans, MRIs, histopathology slides), enhancing diagnostic objectivity and efficiency.
Agriculture and Food: Used for agricultural product grading (by size, color, shape), maturity assessment, pest/disease
identification, food packaging inspection, and production line impurity removal.
Autonomous Driving: Serves as a core environmental perception sensor for vehicles, handling tasks like lane line detection,
traffic sign recognition, pedestrian/vehicle detection, and drivable area segmentation.
Summary
Machine vision fundamentally transforms visual information from the physical world into computer-processable, analyzable
digital data to enable precise decision-making. With precision, speed, and reliability as its core strengths, it continues
expanding beyond traditional industrial inspection into broader recognition, understanding, and interaction scenarios.
With the deep integration of deep learning technologies and continuous advancements in hardware computing power,
machine vision is evolving from “seeing” to ‘understanding’ and “penetrating,” becoming a foundational infrastructure-level
technology driving the development of smart manufacturing, smart cities, and intelligent living. Its future development will
increasingly focus on areas such as 3D vision, multi-sensor fusion, embedded edge computing, and few-shot learning to
address more complex and flexible application demands.