BitFlow Identifies Five Machine Vision Trends for 2023

WOBURN, MA—Machine vision acts as the eyes of the factory, leveraging imaging technology to capture visual information for analysis and rapid decision making. The most common uses of machine vision are for detecting defects, sorting objects, positioning and measuring parts, and guiding automation robots for production control.

Today, the market for machine vision worldwide is over $14 billion (USD). Annual growth is pegged at approximately eight percent for machine vision cameras, lenses, frame grabbers, processors, and software—with no signs of slowing.

BitFlow, a manufacturer of frame grabbers, predicts the following developments will help shape machine vision in 2023 and, in turn, open new opportunities for systems integrators and for the manufacturers who deploy machine vision on their plant floors.

1. Continued Growth: The COVID-19 pandemic rapidly increased the adoption of machine vision technology across a wide swath of industries, a trend that has been accelerated by companies relying on automation to address continuing labor shortages. This pace is expected to continue in 2023 because of many factors including global supply chain issues that hamper production schedules. Manufacturers impacted by supply shortages are replacing offshore suppliers with in-house production of components that require new visual inspection systems.
2. More Advanced Sensors: New imaging sensors are enabling machine vision systems to identify defects in smaller, more complex parts with extremely tight tolerances. Sensor advancements are emerging in the areas of multispectral, UV, SWIR, and hyperspectral designs. Ultimately, more powerful sensors will generate demand for higher performance components to support them, such as lighting systems and frame grabbers.
3. CoaXPress Over Fiber: The updated CoaXPress 2.0 interface enables support for faster, more reliable data transfer between high-resolution cameras and host PCs. Delivering transfers speeds up to 12.5 Gbps, CoaXPress 2.0 is elevating machine vision applications, while reducing system complexity and costs. The next step for CoaXPress is applying the benefits of fiber optic cabling as an alternative to coaxial cable. As an extension of the CoaXPress 2.0 protocol, CoaXPress over Fiber (CXPoF) provides the generic GeniCam programming interface for hardware and software and is independent of any operating system, making it simple to upgrade from coaxial to fiber optics.
4. Deep Learning: By mimicking how the human brain processes visual input, deep learning brings machine vision into industrial applications where highly subjective decisions need to be made, for instance, where there is subtle variability in an image, such as the presence of dust or when distinguishing between very similar objects. Plus, like the brain, it "learns" or self-trains itself from continually analyzing data that create models of what good and bad parts look like. Convolutional neural networks (CNNs) process massive datasets, specifically through the use of complex algorithms rather than exclusively rules-based static algorithms. Deep learning represents an alternative to traditional machine vision, and while it is deployed in few applications today, it has the potential to deliver tremendous business value.
5. Cloud Computing: Machine vision requires high-speed, real-time processing with low latency. At this stage, it is hard to achieve this with cloud computing, so edge computing is typically used to shorten the distance between collected data and processing. However, this is set to change. Although still in the experimental stages, companies using machine vision are beginning to turn to the power of cloud computing to handle the flow of the massive amounts of image data their systems generate. Improved upload speeds are making cloud computing a practical decision for storage and sharing data and, possibly soon, 3D analysis, deep learning, and AI.

Along with relieving humans of the responsibility of manually inspecting products, machine vision has a growing number of other uses including transportation, self-driving cars, research science, and in warehouses where cameras are used to check stock and automatically order replenishment when necessary. The innovations listed here will benefit those use cases and many more just as they will the traditional industrial applications machine vision serves.

To learn more, visit www.bitflow.com.