The Computer Vision Industry in FY 2024/25: Trends and Predictions

Demand for precision imaging solutions is soaring, and the industry is undergoing a transformative journey marked by significant consolidation and strategic acquisitions.

In 2024 so far, we have reported on 6 acquisitions which will impact the machine vision sector, already more than the whole of 2023. This trend underscores the industry’s relentless pursuit of growth, expansion and technological advancement.

Industry consolidation: a sign of maturity and growth

The surge in mergers and acquisitions within the computer vision industry reflects a maturing market characterized by intense competition and rapid technological evolution. Companies are increasingly seeking to strengthen their market positions, expand their product portfolios, and enhance their technological capabilities through strategic acquisitions. This is driven by the growing need to stay ahead of the curve in an increasingly competitive landscape while addressing the evolving demands of customers across various industries.

Key drivers of industry consolidation

Several factors are fueling the wave of consolidation within the machine vision industry. Firstly, the pace of technological innovation is driving companies to acquire specialized expertise and cutting-edge technologies to maintain their competitive edge. Secondly, acquisitions enable companies to broaden their market reach, penetrate new geographies, and tap into emerging market segments, thereby fueling growth and revenue diversification. And, with customers increasingly seeking comprehensive solutions that integrate multiple technologies, companies are compelled to expand their offerings through further acquisition to meet evolving customer needs effectively.

 Looking ahead, we believe the computer vision industry is poised for continued growth and innovation, driven by a number of key trends.

Integration of AI and machine learning

The convergence of computer vision with artificial intelligence (AI) and machine learning (ML) technologies evolved from deep learning and extensive training models will enable more intelligent and autonomous vision systems capable of real-time decision-making and adaptation.

Advancements in edge computing

The proliferation of edge computing capabilities means that vision systems can process and analyze data closer to the source, enabling faster response times, reduced latency, greater security and enhanced efficiency in various applications, from robotics to smart surveillance.

Expansion into new applications

Machine vision technology is extending its footprint into new and diverse applications, including augmented reality, smarter transportation and environmental monitoring to name just a few.

In addition to the overarching trends shaping the industry, we think that a range of emerging imaging technologies will also influence significant advancements and innovation. We’ve looked briefly at a few of them.

Shortwave infrared imaging (SWIR)

Imaging in the shortwave infrared (SWIR) spectrum has gained traction across various industries due to its unique capabilities to penetrate atmospheric obscurants, such as haze, fog and smoke. It can reveal hidden features not visible to the human eye or traditional imaging systems, such as water content in fruit, used to indicate ripeness. SWIR imaging is being more widely adopted in applications such as surveillance, security, agriculture, and medical imaging. With advancements in SWIR sensor technology, including higher sensitivity, improved resolution and lower costs, the implementation of SWIR imaging is expected to proliferate, unlocking new opportunities for innovation and application development.

Event-based sensing

Event-based or neuromorphic sensing represents a paradigm shift in imaging technology inspired by the human visual system. Unlike conventional frame-based imaging systems that capture and process images at fixed intervals, event-based sensors operate on a pixel-by-pixel basis, detecting changes in light intensity asynchronously and in real-time, so will capture an image only when (and as soon as) something within the field of view changes.

This novel approach offers several advantages, including lower latency and low power consumption, making it well-suited for applications requiring fast response times, such as robotics, augmented reality and motion tracking. As research and development in event-based sensing continue to advance, the technology holds immense potential to revolutionize machine vision applications by mimicking the efficiency and adaptability of the human visual system.

LiDAR

LiDAR systems provide detailed 3D mapping of the environment by emitting laser pulses and measuring their reflection times. LiDAR’s ability to detect objects, obstacles, and road conditions in real-time enhances vehicle safety, efficiency, and autonomy. As automotive manufacturers continue to invest in LiDAR technology, advancements such as higher resolution sensors and cost reductions will accelerate its integration into other platforms such as autonomous mobile robots (AMRs) and aerial surveying.

What might be on the horizon?

The landscape of machine vision acquisitions is not only characterized by consolidation among established players but also by the acquisition of niche technologies and specialized expertise. As the demand for innovative solutions grows, companies will increasingly seek to augment their portfolios with specialist technologies to gain a competitive edge and address evolving customer needs. While we don’t have a crystal ball to predict what will come next, we think we’re going to be seeing plenty more acquisitions in specialist areas.

Companies looking to strengthen their capabilities in areas such as robotics, autonomous vehicles, IoT and augmented reality may seek to acquire event-based sensor specialists. By integrating these specialized sensors into their product offerings, companies can enhance the performance and efficiency of their vision systems, unlocking new opportunities for innovation and market differentiation.

Hyperspectral imaging, which captures a vast range of spectral information, offers unparalleled opportunities for material identification, chemical analysis and remote sensing applications. Companies operating in industries such as agriculture, environmental monitoring, and healthcare may target hyperspectral imaging specialists for acquisition to leverage their expertise in developing advanced imaging systems and analytical tools. By integrating hyperspectral imaging technology into their solutions, companies can deliver more comprehensive and actionable insights to their customers, driving value and differentiation in highly specialized markets.

In addition to outright acquisitions, companies may also explore strategic partnerships and collaborations with niche technology providers to access specialized expertise. By forging alliances with startups, research institutions and technology pioneers, companies can accelerate their innovation cycles, mitigate risks and capitalize on new market opportunities. These collaborative efforts will further enable knowledge exchange, resource sharing, and mutual learning, fostering a culture of innovation and entrepreneurship within the machine vision ecosystem.

Exciting times for computer vision

The computer vision industry is experiencing a period of rapid transformation characterized by consolidation and collaboration. As companies continue to navigate this dynamic landscape, strategic acquisitions, partnerships and technological advancements will play a pivotal role in shaping the future trajectory of the industry.

With the convergence of machine vision with AI, edge computing and other emerging technologies, we’re sure that FY24/25 is going to see many more acquisitions and alliances, sign up to our newsletter to keep informed.

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