TRENDS IN MACHINE VISION: AI IS HERE TO STAY
The landscape of industrial automation is constantly evolving, driven by innovations in various fields, with machine vision standing out as a critical component. In recent years, one technology has emerged from the shadows to dominate discussions and drive progress within machine vision: Artificial Intelligence (AI). AI is not merely a passing trend; it has firmly cemented its place as a transformative force, reshaping how we approach inspection, quality control, and automation. This comprehensive overview will delve into the multifaceted trends currently defining machine vision, from the pervasive influence of AI to advancements in cameras, lenses, lighting, software, and the exciting realm of computational imaging, while also touching upon the critical skills necessary for professionals to thrive in this rapidly changing environment.
ARTIFICIAL INTELLIGENCE: THE GAME CHANGER
Without a doubt, artificial intelligence, particularly machine learning and deep learning, represents the most significant and dominant trend in contemporary machine vision. Its integration is no longer a niche feature but a fundamental component found across nearly every major machine vision software package. Even less common software solutions are now incorporating AI capabilities, reflecting a widespread recognition of its potential.
The influence of AI extends beyond software to hardware as well. Many camera manufacturers, in fact, a majority, are now offering camera models with embedded AI capabilities. Some of these cameras provide proprietary AI functions, pre-programmed for specific tasks, while others offer a more open AI engine, allowing users the flexibility to install their own preferred AI software for tailored applications. This trend highlights a move towards decentralized AI processing, bringing intelligence closer to the data source.
Furthermore, the market has seen the emergence of specialized machine vision edge devices. These devices come equipped with AI accelerators embedded directly into their architecture, facilitating the implementation of AI at the “edge”—meaning processing occurs locally, near the cameras and production line, rather than relying solely on centralized servers. This edge processing significantly reduces latency and bandwidth requirements, making real-time AI inferences more feasible in demanding industrial environments. Of course, traditional implementations of AI in the cloud (remote servers) and in the fog (local cloud infrastructure) also continue to play vital roles, offering scalability and distributed processing power.
What does this widespread adoption truly signify for the industry? It underscores a crucial point: AI is unequivocally here to stay. Its primary strength lies in its ability to tackle previously challenging or even intractable applications. Consider the task of flaw detection. Unlike easily recognizable parts, a flaw—such as a scratch or an imperfection—often possesses a unique, unpredictable shape for which no standard template exists. Historically, programming systems to detect such arbitrary flaws was a highly tedious and time-consuming endeavor, requiring extensive testing and constant refinement of complex code. AI, however, can address the challenge of finding flaws in a remarkably straightforward manner. By learning from a sufficiently large and representative dataset of both flawless and flawed samples, AI algorithms can identify subtle anomalies that would be incredibly difficult for conventional image processing methods to catch.
However, it is crucial to acknowledge that while AI offers immense power, it is not a panacea. A common pitfall is the tendency to apply AI to applications that could be solved, perhaps even more efficiently and robustly, by conventional image processing approaches. For instance, if the application simply requires recognizing a manufactured part, there are well-established and highly effective traditional image processing methods already in place. While AI can certainly perform part recognition, it might actually demand more engineering effort, particularly in collecting the necessary training data, than utilizing an existing, proven image processing technique. The key lies in understanding when and where AI provides a genuine advantage.
The current market for AI products in machine vision is characterized by intense competition. Numerous companies, both established giants and innovative startups, are aggressively promoting their AI solutions, leading to a proliferation of promises. It is an inevitable reality that not all these promises will be realized. As expectations sometimes outstrip current capabilities, there will likely be instances where AI is met with skepticism or less welcome due to past failures or unmet expectations. This phase is often referred to as the “valley of disillusionment” in the technology adoption lifecycle. Nevertheless, as the market matures and fewer, more effective players establish traction and build solid track records, the true, tangible benefits of AI will be widely recognized, leading to sustained growth. Looking ahead, we can anticipate the emergence of new AI technologies and methodologies that will further enhance the efficiency, accuracy, and overall effectiveness of AI in machine vision.
ADVANCEMENTS IN CAMERAS AND IMAGE SENSING
Beyond AI, the core components of machine vision systems—cameras and their image sensors—continue to see significant expansion in their available offerings. One prominent trend is the relentless push towards ever higher-resolution image sensors, boasting an increasing number of pixels. Today, 100-megapixel cameras are readily available, and even ultra-high-resolution cameras exceeding 250 megapixels can be found on the market. It is important to note, however, that as image resolution surpasses approximately 20 megapixels, the cost of the camera tends to rise rapidly, reflecting the increased complexity and precision required in manufacturing these advanced sensors and their associated electronics.
Fortunately, the trend toward ever-smaller pixel sizes has, for the moment, decelerated. Smaller pixels have historically presented considerable challenges for optical design, demanding more sophisticated and expensive lenses. Moreover, they typically deliver poorer signal-to-noise performance, which can compromise image quality, especially in low-light conditions. A lower limit for pixel size, around 2.35 micrometers (µm), appears to be the current threshold. Should future technical breakthroughs effectively address these optical challenges and improve the signal-to-noise ratio of smaller pixels, then a renewed trend towards miniaturization would likely become inevitable, potentially leading to less expensive cameras due to smaller sensor fabrication costs, though advancements in optics might offset some of these savings.
Another exciting camera trend is the increasing availability and decreasing cost of Short-Wave Infrared (SWIR) cameras. While SWIR cameras have existed for some time, the introduction of Sony’s IMX990 and IMX991 image sensors marked a significant turning point, ushering in a new generation of SWIR cameras that are considerably more affordable than their predecessors. These sensors are not based on silicon, like most common image sensors, but rather on indium phosphide (InP) and indium-gallium-arsenide (InGaAs) semiconductors. This material difference provides them with a remarkably wide spectral response range, spanning from 400 nanometers (nm) in the visible light spectrum down to 1,700 nm in the infrared. This range is significantly wider than silicon’s typical 200 nm to 1,000 nm range. The declining cost of SWIR cameras is opening up new applications that were previously inaccessible. The ability to image in SWIR wavelengths can reveal useful information not visible to the human eye or conventional cameras, such as detecting subtle bruising on fruit or seeing through colored plastics that appear opaque in visible light.
Three-dimensional (3D) imaging continues to be a growing segment within machine vision, with classical techniques remaining dominant. These include:
- Structured lighting: Projecting known light patterns onto an object and analyzing their distortion to infer depth.
- Laser profilometry: Using a laser line or dot scanner to create a 3D profile of a surface.
- Stereo vision: Mimicking human binocular vision by using two or more cameras to capture images from slightly different angles and then calculating depth from the disparities.
While some novel techniques, such as Time-of-Flight (TOF) imaging, are in limited use, their current resolution limitations often restrict their applicability to specific use cases.
Hyperspectral and multispectral imaging also continue to show great promise for specialized machine vision applications. These technologies capture images across many narrow bands of the electromagnetic spectrum, providing rich spectral information. They are proving invaluable in areas such as:
- Recycling plastics and textiles, where different material compositions have unique spectral signatures.
- Sorting food products based on ripeness, spoilage, or foreign object detection.
- Monitoring agricultural crop health by analyzing plant pigments and water content.
However, the high expense of these cameras and the massive amounts of data they generate (which require significant transmission and processing capabilities) are currently limiting their more widespread adoption across all industrial sectors.
EVOLVING CAMERA INTERFACES
Despite the proliferation of various interfaces for machine vision cameras, a select group of “big five” still largely dominates the industry: Camera Link, GigE Vision, USB3 Vision, CoaXPress, and Camera Link HS. Each offers distinct advantages in terms of speed, cable length, and integration.
Camera Link: Although an older technology, Camera Link remains remarkably popular due to its robustness and established ecosystem. However, no further significant development of this interface is anticipated, suggesting its role might gradually diminish as newer, faster technologies mature.
GigE Vision: This interface continues to progress rapidly, with speeds dramatically increasing from its initial gigabit Ethernet capabilities. Current implementations offer speeds of 5 Gbps, 10 Gbps, 25 Gbps, and even up to 100 Gbps. While increasing speeds may necessitate shorter cable lengths in some scenarios, GigE Vision still offers one of the longest camera interfaces, capable of reaching up to 100 meters initially. For speeds exceeding 10 GigE, the underlying protocol is transitioning from User Datagram Protocol (UDP), which lacks data packet resend capabilities, to Remote Direct Memory Access (RDMA). RDMA is significantly more efficient in processor utilization and crucially includes packet resend capabilities, ensuring greater reliability at these higher data rates, which is essential for industrial applications.
USB3 Vision: Since its inception, USB3 Vision has undergone two significant speed doublings, progressing from 6 Gbps to 20 Gbps. The impending arrival of USB4 promises even higher speeds, potentially reaching up to 40 Gbps. Despite these advancements in the underlying USB standard, the machine vision interface will continue to be known as USB3 Vision, maintaining continuity for developers.
CoaXPress: The current version of CoaXPress, version 2.1, delivers impressive bandwidth of up to 12.5 Gbps over a single coaxial cable. By utilizing four parallel cables, this can be scaled up to a formidable 50 Gbps speed, making it suitable for high-resolution, high-frame-rate applications. Version 3 is already in the planning stages and is expected to push towards even higher speeds, further cementing its position for demanding scenarios.
For embedded systems, where space and power efficiency are paramount, the MIPI (Mobile Industry Processor Interface) and SVLS (Standardized Vision Link System) interfaces are gaining considerable popularity. These interfaces are designed for direct integration with system-on-chip (SoC) architectures, commonly found in compact, low-power vision modules.
LENS INNOVATIONS: CLARITY AND PRECISION
While lenses, governed by the immutable laws of physics and the available glass types, typically advance at a slower pace than other machine vision components, there have nonetheless been notable and impactful innovations. A significant advancement is the availability of more lenses ruggedized for challenging industrial environments. These lenses are specifically designed to withstand moist conditions, mechanical shock, and vibrations, ensuring reliable performance in harsh factory settings where standard optics might fail.
Lenses have also seen advancements in their ability to handle the demands of smaller pixels and larger image sensor sizes. These improvements primarily stem from more sophisticated lens designs and increasingly precise manufacturing techniques, ensuring that even the most minute details captured by high-resolution sensors are transmitted with clarity.
New types of lenses have also emerged to enable novel applications:
- Visible through Near-Infrared (NIR) lenses: These lenses are designed to image across a broad spectrum from 400 nm (visible light) through 1,000 nm (near-infrared), maintaining excellent resolution and minimizing chromatic aberration. This wide spectral range aligns well with the response capabilities of silicon sensors, opening up more applications that require imaging in both visible and NIR light simultaneously.
- SWIR lenses: Complementing the growing availability of SWIR cameras, the range of lenses supporting SWIR imaging is also expanding. As the volume of these specialized lenses increases, their price is gradually decreasing, making SWIR imaging more accessible to a broader range of industrial users.
- Telecentric lenses: Offerings of telecentric lenses are increasing significantly due to a heightened interest in their unique advantages for making precision measurements. Unlike conventional lenses, telecentric lenses eliminate perspective errors, ensuring that the magnification of an object remains constant regardless of its distance from the lens, which is crucial for accurate dimensioning and gauging.
To facilitate more compact vision systems, there is a growing interest in wide-angle lenses. Efforts are actively underway to mitigate the inherent lens distortion common in wide-angle optics. Both optical techniques (through complex lens element arrangements) and software-based correction methods are under continuous development to provide clear, undistorted wide-angle views. Furthermore, the increasing interest in liquid lenses is opening up more opportunities for adjusting focus dynamically, either programmatically (via software control) or automatically (in response to system feedback), enhancing flexibility in diverse applications.
THE EVOLUTION OF MACHINE VISION LIGHTING
Lighting, a fundamental yet often underestimated element of any machine vision system, continues to make steady evolutionary progress. While halogen lighting was once common, very few machine vision applications today utilize it. The impending ban of halogen lamps in Europe within the next couple of years, with exceptions only for certain exempt applications, will further accelerate their phased removal from the market.
Light Emitting Diode (LED) illumination continues to overwhelmingly dominate machine vision lighting and is expected to do so for the foreseeable future. LEDs are constantly becoming more efficient, allowing for higher light output with a given amount of input power. This not only saves energy but also enables brighter illumination for challenging imaging tasks. A key technological advancement in LED lighting is the increasing popularity of Chip-on-Board (CoB) technology among light source manufacturers. CoB designs allow for a much higher packing density of individual LED chips onto a substrate, leading to more compact and powerful light sources. This technology also facilitates better thermal management, which is crucial for maintaining LED longevity and consistent light output, ultimately contributing to higher overall light output from LED light sources.
SOFTWARE: EASE OF USE MEETS FLEXIBILITY
The machine vision software landscape continues to exhibit a clear bifurcation, catering to two distinct user needs: ultimate ease of use and maximum flexibility. For developers prioritizing ease of use, modern software packages increasingly provide guided forms and intuitive graphical interfaces. These platforms enable the creation of sophisticated machine vision applications using low-code or even no-code solutions. This approach significantly lowers the barrier to entry, freeing developers from the need for deep theoretical knowledge of image processing algorithms or extensive computer coding skills. It empowers a wider range of engineers and technicians to implement vision solutions.
Conversely, for those requiring maximum flexibility and control, many machine vision software packages also offer direct access to their Software Development Kits (SDKs). An SDK is a comprehensive library of image processing functions and tools that can be linked with a user-written program. This allows experienced developers to address highly specific and complex application needs with bespoke code, providing granular control over every aspect of the vision system.
As new technologies, particularly AI, are introduced and matured, there will be a continuous refinement and creation of specialized tools tailored for specific applications utilizing these advancements. These application-specific tools will eventually relieve vision system developers from a significant amount of the intricate work involved in applying cutting-edge technologies, making advanced capabilities more accessible and efficient to deploy across various industries.
THE RISE OF COMPUTATIONAL IMAGING
An emerging and increasingly significant field within machine vision image processing is computational imaging. This relatively new arm of machine vision involves the algorithmic combination of multiple images to create a single, more useful, or information-rich image that cannot be obtained from a single capture. Unlike traditional image processing that works on a single image, computational imaging leverages data from several images to reconstruct or enhance visual information.
While some techniques might technically fit this description, like structured light imaging for 3D surfaces, they are often considered foundational rather than solely computational imaging. In the common approach to structured light, a series of patterns (e.g., bars of light) are projected onto a scene, and images are captured for each pattern. By algorithmically combining all these images, a detailed 3D profile of the imaged surface can be generated as a single, comprehensive representation.
More distinctly, an emerging use of computational imaging is photometric stereo. This technique is particularly effective at detecting subtle surface artifacts that are not easily imaged with other methods. It typically uses three or more, but commonly four, images. Each image is acquired with illumination coming from a different direction. By analyzing the varying light reflections across these images, the system can compute a detailed image showing reflectance properties or minute surface height changes, effectively revealing fine structures such as defects, textures, or even print quality variations that might otherwise be invisible.
Another compelling instance of computational imaging is super-resolution. In this technique, multiple images of the same scene, captured from slightly different positions, are combined to create a single image with significantly higher resolution than any of the individual input images. This can be achieved in two primary ways:
- The different images can come from individual cameras positioned to take images simultaneously from slightly varied viewpoints.
- Alternatively, the images can be acquired from the same camera, with the image sensor physically shifted by a fraction of a pixel in different directions between each exposure, effectively capturing sub-pixel information.
Super-resolution techniques promise to push the boundaries of achievable detail without requiring extraordinarily expensive high-resolution sensors, making them valuable for inspection tasks demanding extreme precision.
NAVIGATING THE FUTURE: ESSENTIAL SKILLS FOR MACHINE VISION PROFESSIONALS
As machine vision technology continues its rapid evolution, particularly with the transformative influence of AI, the skills required for professionals in this field are also shifting. To succeed in this dynamic environment, machine vision engineers, integrators, and developers must embrace continuous learning and develop a multifaceted skill set. The age of AI in machine vision demands not just technical proficiency but also strategic thinking and adaptability.
Here are some essential skills for navigating the future of machine vision:
- AI Literacy and Application Nuance: It’s no longer enough to just know AI exists. Professionals must understand the fundamental principles of machine learning and deep learning, including various neural network architectures. Crucially, they need the discernment to know *when* to apply AI versus when conventional image processing methods are more appropriate or efficient. This requires a deep understanding of problem types and the strengths and weaknesses of different algorithmic approaches.
- Data Management and Curation: The success of AI in machine vision heavily relies on the quality and quantity of training data. Professionals will need strong skills in data collection, annotation, preprocessing, and managing large datasets. Ensuring data is representative, balanced, and clean will be paramount for developing robust AI models.
- Hardware Expertise Across New Frontiers: While software is gaining prominence, a solid understanding of the underlying hardware remains vital. This includes familiarity with new camera types like SWIR, advanced 3D sensors, and hyperspectral imagers, as well as the nuances of high-speed camera interfaces (GigE Vision, CoaXPress). Understanding how different lenses and lighting configurations impact image quality and data capture is also non-negotiable.
- Hybrid Software Proficiency: The future demands versatility. Professionals should be comfortable with both low-code/no-code vision platforms that accelerate deployment and possess the deeper programming skills (e.g., Python, C++) to leverage SDKs for highly customized solutions. The ability to integrate AI models into existing or new software architectures will be a key differentiator.
- Interdisciplinary Problem Solving: Machine vision increasingly draws from diverse fields. Skills in computational imaging, optics, robotics, and even data science will allow professionals to combine various techniques to solve complex challenges. For instance, knowing how to integrate photometric stereo with conventional defect detection or super-resolution with high-resolution inspection.
- System Integration and Connectivity: As machine vision systems become more intertwined with broader industrial automation, skills in system integration, networking, and understanding protocols like OPC UA or MQTT will be essential. The ability to connect vision data seamlessly into Manufacturing Execution Systems (MES) or Enterprise Resource Planning (ERP) will unlock significant value.
- Troubleshooting and Debugging in Complex Systems: With more sophisticated components and software, diagnosing issues will require a holistic understanding of the entire vision pipeline, from illumination and optics to sensor performance, processing algorithms, and data output.
- Adaptability and Continuous Learning: Perhaps most importantly, the pace of innovation in machine vision dictates a mindset of perpetual learning. Professionals must stay updated on new research, emerging technologies, and best practices to remain competitive and effective.
CONCLUSION
While machine vision has undoubtedly matured significantly since its inception in the late 1970s, it is clear that the journey of progress is far from over. The industry continues to realize remarkable advancements, driven by relentless innovation and a unique ability to draw upon technologies developed in adjacent fields. The cross-pollination of ideas and techniques from photography, military reconnaissance, medical imaging, remote sensing, and various other imaging applications consistently enriches machine vision, leading to the evolution of newer, more sophisticated, and more effective approaches to solving the intricate demands of industrial automation. With AI firmly established as a core enabling technology and ongoing breakthroughs across all system components, machine vision is poised to continue its vital role in enhancing efficiency, quality, and precision across countless industries worldwide.