The latest technological trends impacting the machine vision inspection industry include embedded vision, deep learning, and the effectiveness of invisible light imaging.
With technological advancements and the development of smart factories, machine vision inspection equipment has undergone tremendous changes over the past decade. The global market for visual inspection equipment is projected to nearly double to $ 13.62 billion by 2022, driven by increasing industry demands for quality inspection. The Asia-Pacific region will continue to be the largest market globally, accounting for 38.4 % of revenue by 2022. Appearance inspection suppliers demonstrating high innovation in technology, flexibility, efficiency, and accuracy will achieve the greatest success in this evolving market.
While global economic uncertainty will impact the visual inspection industry, industry associations and experts generally believe it will not cause serious damage. Hardware platforms such as embedded vision, powered by deep learning software, are expected to thrive, and traditional factory-level applications will maintain strong growth. Below, we'll examine three major trends influencing the visual inspection industry.
I. Embedded vision will continue to grow.
Embedded vision will continue to grow rapidly, thanks to support from a growing number of industry applications, such as autonomous driving, life sciences, consumer electronics, border surveillance, and agriculture.
Processing power has been greatly enhanced, and memory has become extremely cheap. Users can choose a very small camera and use cloud data from various sources. Combining these factors with machine learning offers an inherent vision, even when using separate software packages.
The customer wanted a system integrator to develop an entire embedded vision system for them. Embedded vision enables smart cameras to achieve their original purpose: image processing and video analysis within a very small enclosure, as close as possible to the image sensor. In response to the embedded vision market, we developed application-specific solutions for rapid delivery in a low-cost, low-power platform—from camera design to FPGA programming—that integrates artificial intelligence and deep learning capabilities.
Designing an attractive system for customers is the biggest challenge in embedded vision. It's a daunting task to pack all the functionalities of a customer's appearance inspection into a tiny size using a low-cost, low-power device. Introducing a completely different hardware solution to consumers is no easy feat, but the ultimate hope is that customers will somehow produce more user-friendly, smaller, and ultimately lower-cost products.
In many use cases, traditional visual inspection cannot compete with embedded vision.
II. Further Applications of Deep Learning
Deep learning for visual inspection has been at the forefront of disruptive technologies. If you're involved in the visual inspection industry, you've likely seen how this software integrates with deep learning algorithms and how quickly it produces results. These systems can run thousands of permutations and achieve 100 % accuracy in visual inspection for recognition, historical records, and other applications.
Deep learning will have a profound impact on traditional image analysis methods. This will not only change the products we produce, but also the way we interact with our customers. Deep learning will play a crucial role in solving applications where traditional visual inspection is inadequate. For example, when inspecting vaccines in freeze-dried vials, the results vary greatly each time, largely depending on their drying method. Traditional inspection processes are extremely challenging because, in some cases, particles may look very similar to cracks, while deep learning helps to distinguish these subtle differences.
III. Improving the efficiency of invisible light imaging
While deep learning may be the latest approach to gathering information from images, it is not the only option. Advances in short-wave infrared cameras and illumination have improved the efficiency of imaging the invisible. In these higher wavelength environments, you can achieve a wider range of applications, such as detecting defects inside composite materials in aircraft wings. We are now introducing high-power SWIRLEDs to the high-speed visual inspection application market.
The demand for hyperspectral imaging is constantly growing. When you're observing hundreds of spectral bars over a wide area to detect subtle differences between objects, you need a broadband light source. This will allow us to reduce the number of LEDs we use and create a broadband light source that mimics halogen lamps.
Challenges of visual inspection
Smart sensors, smart cameras, and configurable vision systems have largely eliminated the need to develop vision inspection systems. The most common applications today are accomplished through plug-and-play technologies. Over the past decade, smart cameras have become increasingly powerful, and lighting companies have expanded their product offerings. However, despite the increasing functionality and decreasing prices of software packages, issues remain regarding interconnectivity and standardization.
Different companies use different terminology for the same thing. Even standardized communications like Ethernet vary greatly between companies, and there's been no real push for open software standards in the vision industry.
Today's vision products can meet the needs of most applications. However, as technology and customer demands evolve, system integrators must remain vigilant. For example, in the 3D imaging market, hardware innovation precedes software innovation.
Although many 3D sensors and cameras are available, such as laser triangulation and stereo sensors with pseudo-random pattern generators, there is a significant gap in development toolchains for rapid system development.
For example, many OEMs currently use open-standard 3D sensors, writing program applications from scratch, or using " closed " systems for tool configuration, which is often expensive. High-speed airborne image processing may require 3D sensors with field-programmable gate arrays ( FPGAs ) , allowing non- FPGA programmers to deploy 3D image processing algorithms in software packages .
Another challenge is the ability to extract information from artificial intelligence and deep learning. The biggest challenge is distinguishing between hype and substance. The reality is that " many AI and deep learning algorithms are sometimes too cumbersome."
While visual inspection applications benefit from deep learning algorithms, these algorithms cannot solve all problems. This is especially evident when comparing the effort required to achieve over 99 % accuracy compared to traditional programming. Nevertheless, this technology does hold its own and will continue to play a significant role in the coming years.
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