Machine Vision Technology: What Does It Entail? — How Machines Are Changing the World with “Sight”

Today, let’s dive into a seemingly high-tech but actually ubiquitous topic in our daily lives—machine vision. Many people might wonder: Isn’t machine vision just a camera plus AI? Why can it inspect products in factories 100 times faster than humans? How do self-driving cars use it to avoid obstacles? Or why can your vacuum robot suddenly navigate around a shoe on the floor? Don’t worry; this article will break down the “inner workings” of machine vision in the most down-to-earth way, showing you how it transforms machines from “blind” to “Sherlock Holmes.”

Machine Vision: Equipping Machines with “Human Eyes and Brain”

If we were to personify machine vision, it would be the combination of super vision and a super scholar.
Eyes: Industrial cameras, LiDAR, infrared sensors… These devices act as the “eyeballs” of machines, responsible for capturing images.

Brain: Algorithms and processors analyze the images, such as determining “Is there a scratch in this picture?” or “Is that object a cat or a dog?”
Nerves: Communication modules transmit the analysis results to other devices, for example, telling a robotic arm, “Quick, throw out this defective product!”

Compared to human vision, where does it excel?

For example, when you stare at a phone screen to find dead pixels, you might get eye strain after just 10 minutes. In contrast, a machine vision system can work 24/7 without blinking, scanning 100 screens per second, and even detecting dead pixels at a micron-level precision that is invisible to the naked eye.

Dissecting the “Three Major Components” of Machine Vision

Hardware: The “Sensory Organs” of Machines

• industrial Camera: Not just an ordinary camera! Industrial cameras can have resolutions as high as 500 million pixels (100 times stronger than a smartphone) and can capture X-rays and infrared light.
• Lighting: Lighting is not just for making photos look good! For example, blue light can highlight metal scratches, and backlighting can reveal missing corners of pills.
• Processor: Chips like NVIDIA’s Jetson and Huawei’s Ascend series are specifically “trained” for image processing, with speeds comparable to Formula 1 racing cars.

Fun fact: Some fruit sorting machines use near-infrared light to “see through” apples to determine sugar content and whether there are any wormholes in an instant!

Software: The “Super Brain” of Algorithms

• Preprocessing: Like a beauty filter, it first removes noise and adjusts contrast in images (e.g., making a blurry license plate clear).
• Feature extraction: Teaching machines to recognize key points—such as finding edge burrs when inspecting chips or focusing on eye spacing when recognizing faces.
• Deep learning: Training AI models to be “experts in authentication,” for example, teaching them that “authentic LV patterns look like this, while counterfeit ones have a 0.5-millimeter deviation in texture.”
• Practical case: A lithium battery factory used the YOLO algorithm to detect defects in battery electrodes, reducing the false detection rate from 15% to 0.3% and avoiding losses of 200 million yuan per year!
• System Integration: Enabling “Hand-Eye Coordination” in Machines

Just seeing is not enough; the key is to “act on what you see.” For example:

When a vision system detects a crooked label on a medicine bottle, it notifies the robotic arm to kick it off the production line within 0.1 seconds.
Self-driving cars must brake within 100 milliseconds after recognizing a red light; otherwise, you know what could happen.

The “Job Map” of Machine Vision: Ubiquitous from Factories to Fields

Industrial Quality Inspection: A Solution for All Problems

• Case: A mobile phone glass cover factory
• Pain point: human eye inspection with scratches missed up to 30%, customer complaints;;
• Solution: Deployed 16 line-scan cameras + AI inspection system;
• Result: Inspection speed increased to 0.8 seconds per piece, defect detection rate reached 99.99%, and annual compensation reduced by 5 million yuan!

Agriculture: Health Checkups for Crops

• Case: DJI agricultural drones + multispectral cameras
• Operation: Fly over the farmland to generate a crop health map (nitrogen-deficient areas marked in red, pest-infested areas in yellow)
• Benefit: A farm in Jiangsu reduced fertilizer use by 40%, increased rice yield by 12%, and earned an extra 200 yuan per mu.

The Double-Edged Sword: The Pros and Cons of Machine Vision

Why we like machine vision and its advantages:

• Productivity: Factories using machine vision have increased production speed by 50% and reduced errors by 90%.
• Safety: unmanned vehicles can perform firefighting tasks, reducing risks for firefighters.
• Sustainability: Waste reduction (for example, precise material cutting can save 15% of steel).

The cons:

• Job Displacement: By 2030. machine vision may lead to the disappearance of over 20 million manufacturing jobs.
• Privacy Risks: The proliferation of surveillance cameras has raised concerns about potential privacy violations.

Opportunities and risks always coexist. The convenience and opportunities brought by machine vision outweigh its risks and drawbacks. If used properly, machine vision will further liberate human productivity.