Three Major Trends Impacting the Machine Vision Inspection Industry

       As the machine vision industry in China experiences rapid growth, machine vision systems have significantly enhanced the automation of large-scale, continuous production, greatly improving production efficiency and product accuracy. The ability to acquire information and automatically process it has become exceptionally fast, providing an effective way for the integration of information in industrial production. However, three major trends influencing the machine vision inspection industry should not be overlooked.

1. Continued Growth of Embedded Vision:

    Thanks to increasing support from various industry applications such as autonomous driving, life sciences, consumer electronics, border control, and agriculture, embedded vision will continue to grow rapidly. With significantly enhanced processing power and affordable memory, users can opt for very small cameras and utilize cloud data from different sources. The integration of machine learning with embedded vision allows for intrinsic vision if a separate software package is used. Customers expect system integrators to develop entire embedded vision systems for them. Embedded vision enables smart cameras to fulfill their original intent—performing image processing video analysis in a very small housing as close to the image sensor as possible. In response to the embedded vision market, we have developed solutions tailored to specific applications to be quickly delivered on low-cost, low-power platforms (from camera design to FPGA programming) that can integrate artificial intelligence and deep learning capabilities.

   Designing an appealing system for customers is the biggest challenge of embedded vision. Achieving all the features of customers in appearance detection with low-cost, low-power devices is a daunting task. Introducing an entirely different hardware solution to consumers is not easy, but the ultimate hope is that customers will somehow produce products that are more user-friendly, smaller, and ultimately lower in cost.

   In many use cases, traditional vision detection cannot compete with embedded vision.

2. More Applications of Deep Learning:

   Deep learning for visual detection has been at the forefront of disruptive technology. If you are involved in the visual detection industry, you may have witnessed how this software integrates with deep learning algorithms and rapidly produces results. These systems can run thousands of permutations and have 100% accuracy in visual inspections for applications such as identification, history logging, and more.

   Deep learning will have a profound impact on traditional image analysis methods, not only changing the products we produce but also altering how we interact with customers. Deep learning will play a crucial role in solving applications that traditional visual detection cannot address. For example, detecting vaccines in small bottles during freeze-drying, where results vary greatly depending on their drying method, is challenging with traditional detection processes. In some cases, particles may look very similar to cracks, and deep learning helps distinguish these subtle differences.

3. Enhancing Efficiency of Infrared Imaging:

    While deep learning may be the forefront of collecting information from images, it's not the only option. Advances in shortwave infrared cameras and lighting have improved the efficiency of invisible imaging. In these higher wavelength environments, more applications can be achieved, such as discovering defects inside composite materials of aircraft wings.

   The demand for hyperspectral imaging is continuously growing. When observing hundreds of spectral stripes over a large range to detect subtle differences between objects, a broadband light source is needed. This allows us to reduce the number of LEDs used and create a broadband light source simulating halogen lamps.

Challenges in Visual Inspection:

    Smart sensors, smart cameras, and configurable vision systems have greatly eliminated the need to develop visual inspection systems. Today's common applications are achieved through plug-and-play technology. In the past decade, smart cameras have become increasingly powerful, and the product range offered by lighting companies has expanded. However, despite the enhancement of software functionality and the continuous decrease in prices, the interconnection and standardization of software packages remain problematic.

   Different companies use different terms for the same thing. Even standardized communication, such as Ethernet, has significant differences between companies, and there has not been a real push for open software standards in the vision industry.

   Current visual products can meet the needs of most applications. System integrators must stay vigilant as technology and customer demands evolve. For example, in the 3D imaging market, hardware innovation precedes software innovation.

   While there are many 3D sensors and cameras available, such as laser triangulation, stereoscopic sensors with pseudo-random code generators, to achieve rapid system development, there is a significant gap in the development toolchain.

   For example, many OEMs currently use open standard 3D sensors, write program applications from scratch, or use "closed" systems for tool configuration, which is often expensive. High-speed onboard 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 greatest challenge is distinguishing hype from substance. The reality is that "many artificial intelligence and deep learning algorithms can sometimes be too cumbersome."

   While visual detection applications benefit from deep learning algorithms, these algorithms cannot solve all problems. This is particularly evident when people strive for efforts required to achieve accuracy of 99% or more compared to traditional programming. Nevertheless, this technology does have its place and will continue to play a significant role in the coming years.

   Xinxiwang now has its core products, high-precision alignment systems, AOI detection systems, code reading-automatic posting systems, BC systems (equipment line management systems Block Control System), providing reliable and efficient visual solutions for large enterprises, covering areas such as panels, electronics, automotive, packaging, pharmaceuticals, semiconductors, and mechanical manufacturing.