What Is Machine Vision?
Simply put, machine vision technology allows industrial equipment to “see” what it is doing and make rapid decisions based on what it sees. The most common uses of machine vision are visual inspection and defect detection, positioning and measuring parts, and identifying, sorting, and tracking products.
Machine vision is one of the founding technologies of industrial automation. It has helped improve product quality, speed production, and optimize manufacturing and logistics for decades. Only very recently, this proven technology is merging with artificial intelligence (AI) and leading the transition to Industry 4.0, a new era of greater automation and machine intelligence in industrial settings.
Classic Machine Vision Systems
Machines could “see” before AI and machine learning. In the early 1970s, computers began using specific algorithms to process images and recognize basic features. For example, a computer could recognize text, but only if the text was simple and sharp, like a bar code. Shapes had to be predictable and fit an exact pattern. A classic machine vision system couldn’t read handwriting, decipher a wrinkled label, or tell an apple from an orange.
How Does Machine Vision Work?
Machine vision systems ingest images and video streams from specialized cameras, then analyze those images using deep learning algorithms. These algorithms evaluate groups of pixels and determine, based on their training data, what the object is.
Machine Vision vs. Computer Vision
Computer vision is a broad term that refers to leveraging computers or AI to understand and interpret visual data under a variety of conditions. Computer vision is used in a wide range of applications, from medical imaging to retail analytics and facial recognition.
Machine vision, on the other hand, is specifically engineered for predefined tasks like quality inspection, part identification, and robotic guidance in manufacturing settings. With machine vision, robots can make automated decisions in near-real time, processing high volumes per minute accurately and consistently.
AI and Industrial Machine Vision
Increasingly powerful edge computing, plus a growing universe of deep learning AI models—also referred to as neural networks—are radically expanding what machine vision can do. For example, there are models for detecting dead and off-color pixels in on-screen displays, seeing voids in welds, and pinpointing pulled threads in fabric.
The ability to process data directly at or near the point of generation, closer to the camera, allows for reduced latency and bandwidth requirements for industrial systems, making them more reliable, responsive, and cost efficient.
Role of Machine Vision in Robotics
With AI-powered machine vision, robots can perceive in three dimensions, hold parts for one another, and check each other’s work. They can even interact with human coworkers and help ensure they work together safely.
Machines with smart vision can use natural language processing to read labels and interpret signs. Robots with smart vision can understand shapes, calculate volumes, and pack boxes, trucks, and even shipping containers with minimal wasted space.
This shift, from machines that can automate simple tasks to autonomous machines that can see beyond what the human eye can see and think independently, will drive new levels of industrial innovation.
Benefits of Machine Vision
Industrial machine vision helps organizations maintain consistent, high levels of speed, safety, and precision. Because of this, it has become hugely beneficial for smart manufacturing and operations.
Benefits of Machine Vision in Smart Manufacturing
Machine vision applied to smart manufacturing can help improve product quality and overall system efficiency, increasing the throughput of manufacturing lines, reducing labor costs, and freeing up staff to focus on higher-value work. Sensor data and imaging capabilities also help reduce human error with enhanced precision.
In tightly regulated industries like pharmaceuticals, machine vision provides constant checks on product contents, packaging, and labeling for quality assurance. When applied to supply chains, machine vision can automatically scan and track items at each point of the workflow, providing an accurate, moment-in-time account of your inventory.
Benefits of Machine Vision in Operations
Machine vision technologies continuously collect real-time, actionable data. By continuously analyzing data from cameras, microphones, and sensors embedded in industrial equipment and machines, industrial PCs can use AI to detect faults and signs of wear before failure so preventive maintenance can be planned in advance, eliminating unexpected downtime and spreading maintenance costs over time.
Benefits of Machine Vision in Safety and Security
Improving worker health and safety is a critical benefit of applying machine vision to operations. AI-powered computer vision can ensure workers wear proper safety equipment and stay within designated safety zones. Robots, automated vehicles, and equipment with machine vision can recognize and avoid hazards, helping prevent accidents before they happen. If a situation is unsafe, machine vision‒enabled appliances can warn the operator or even be designed to shut down automatically.
In the areas of asset management and security, machine vision can detect and track objects in video feeds to ensure proper use and storage. It can also alert management if assets leave a predefined boundary. Security camera systems can become active security partners capable of controlling building access and identifying dangerous scenarios.
Challenges of Deploying Machine Vision
While machine vision technology provides several key benefits and supports advanced workflows, there are still a number of challenges when it comes to implementation. Organizations will need to integrate new technology investments into their production lines. They will also need to train and deploy machine vision AI models for their specific use case.
The enhanced productivity, consistency, and quality that machine vision can produce are often enough to help offset initial costs and encourage a strong return on investment (ROI). In some cases, manufacturers may also be able to use their existing infrastructure with video capture equipment to start running machine vision workloads now and layer in upgrades over time.
Machine Vision Use Cases
Machine vision has become crucial to operations across several industries. Here are just a few use cases in which machine vision is a cornerstone to success.
Industrial Manufacturing
On the factory floor, machine vision systems perform high-speed quality control and inspection tasks like capturing and analyzing images to detect defects, verify assembly, and guide robotic operations.
Warehouse Automation
Machine vision systems guide automated mobile robots (AMRs) and robotic arms in tasks like bin picking, package sorting, inventory management, and determining the optimal grip points and orientations for handling.
Visual Inspection
Machine vision can also perform automated visual inspections through high-resolution product images, which are then analyzed using AI to detect defects, measure dimensions, and ensure quality standards.
Autonomous Driving
Machine vision with multiple cameras works alongside other sensors such as LiDAR to help autonomous vehicles develop a 360-degree understanding of their environment. This technology helps vehicles detect and classify objects like other vehicles, pedestrians, and obstacles while also determining their relative distance.
The Future of Machine Vision
The future of machine vision is driven by the continual advancements in AI and edge computing, which are creating new worlds of possibility with every passing year:
- 3D machine vision uses visual inputs from multiple angles to perceive objects and environments with enhanced spatial understanding. Robots and industrial appliances can use 3D machine vision to more precisely measure volume, depth, and surface metrics that 2D systems might miss.
- Hyperspectral imaging captures and analyzes data across multiple wavelengths of light, enabling the detection of properties invisible to conventional cameras. When used with machine vision, it can catch things like chemical composition, ripeness in food products, material identification, and subtle defects in manufacturing based solely on unique spectral signatures.
- Generative AI (GenAI) can be used with machine vision to enable robotics to both recognize and verbalize what they see, allowing for new ways of interacting with autonomous systems.
