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From Onshoring to AI: Trends Impacting Machine Vision
FOCUS 2025 to highlight the latest in machine vision and AI-driven automation
In the fast-moving automation landscape, keeping up with technological and application advancements can be taxing. As A3’s FOCUS: Intelligent Vision & Industrial AI Conference approaches, let’s look at some recent trends driving the machine vision industry. Here’s a rundown of topics discussed on Automate LIVE 2025 that are making the most impact — from market demands to artificial intelligence (AI).
Impacts of Onshoring
Recent efforts to bring manufacturing back to the United States are one of the main drivers behind the growth of the overall automation market. Automation systems featuring robots and vision technology will be key to making onshoring efforts work for everyone through improved quality control and data insights. These technologies will help lower prices for consumers while increasing productivity for integrators and end users.
Improved Component Performance
From lighting and filters to cameras and chips, machine vision component performance is reaching new heights every day as components become more accessible and less expensive than ever before in response to ever-evolving customer demands.
Across the board, manufacturers are improving the ease of use and interoperability of vision components for seamless connection and communication. This ensures that systems can be used by nontechnical operators and enables effective communication between cameras, lights, and other components in the background of a process.
Optical components are rapidly evolving to keep pace with camera advances, as new custom shapes, sizes, sensitivities are required, all at shorter lead times. LEDs are also experiencing a spike in demand due to Europe’s ban on halogen as a source for hyperspectral and multispectral imaging. Additionally, nonvisible imaging technologies — particularly in multispectral imaging — are beginning to become more commonplace, led by developments in lower-cost, more compact SWIR image sensors.
Smarter CMOS sensors that can store and dump data at high rates, coupled with computational imaging, are reducing exposure times for quicker image capture and processing. With computational imaging tasks happening on the chip itself, space is freed up in the system and users don’t have to sacrifice onboard compute power for speed. When put into an embedded system, latency can be reduced.
Emerging Applications
As vision system component technology advances, it can be applied to a wider range of applications — on and off the manufacturing floor. Additionally, the machine vision community is increasingly collaborating to enable more specialization for new, unique applications. Here are a few:
- Smart farming: Vision systems are being used in vertical and smart farming operations to reduce chemical usage, monitor crop health, assist with robotic crop harvesting, and protect the environment.
- Food production: Machine vision is helping food processors increase available food stores and improve overall food safety and quality control through advanced inspection and track-and-trace technologies.
- Medical applications: Multispectral and hyperspectral imaging are gaining ground in medical applications, such as noninvasive disease detection via tissue analysis.
- Autonomous drones: Drones are utilizing machine vision technologies for a variety of applications, including agricultural inspection, automated delivery, and search and rescue.
- Augmented and virtual reality: Machine vision technology is being increasingly deployed in AR/VR applications for real-time data overlayed in the physical world.
- Humanoid robots: Machine vision components are enabling exciting advancements in humanoid robots for use in industry and at home, like navigation and obstacle avoidance, which when combined with AI, can help humanoids learn about and interact with their environments.
Growing Capabilities With AI
Although AI can enhance capabilities for many applications, the machine vision market is challenged to keep customer expectations in line with currently available capabilities. Finding the right job for AI in machine vision involves a thorough application analysis. AI especially excels in applications that can’t be handled by existing rules-based processing, such as defect analysis and assembly verification.
While potential is vast, getting the basics of machine vision right is essential for AI to benefit a process. AI for vision applications is only as good as the image data it receives. Each vision system component plays into this process, and the “right image” varies from application to application.
The optimal image must have sufficient contrast to highlight features to be detected relative to the background. If a machine vision system involves AI, the model must be trained on repeatable images so the software can learn to identify “good” and “bad” features. This helps in machine vision corner cases, where rules-based algorithms can struggle.
See the Latest AI and Vision Technologies at FOCUS
You can learn more about these trends and discover the latest in machine vision and AI-driven automation at FOCUS, the Intelligent Vision & Industrial AI Conference. Taking place September 24-25 in Seattle, Washington, this event brings together industry leaders and tech innovators to explore the latest in machine vision, computer vision, and AI-driven automation.
Don’t miss out on your chance to learn about the latest technologies and applications. Register for FOCUS now.
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