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When the technology matures, will machine vision become standard in factories?

When the technology matures, will machine vision become standard in factories?

Jul 12, 2025

As the wave of intelligent manufacturing sweeps the world, machine vision is no longer just a detection device in the factory. It is gradually evolving into the core perception engine of the entire industrial system. If the robot is the "hand" and the control system is the "brain", then machine vision is the "eye". And this pair of "eyes" is undergoing a profound evolution - from passive "seeing" to active "understanding".

In recent years, with the advancement of image processing algorithms, the popularization of deep learning models, and the continuous improvement of hardware performance, machine vision has gradually moved from laboratories to factories, from dedicated equipment to general platforms, and from small-scale customization to standardized products. Today's machine vision system can not only achieve high-precision dimensional measurement and defect recognition in complex scenes, but also has the capabilities of real-time analysis, adaptive adjustment, and multimodal fusion, truly entering the "intelligent era".

Does this mean that machine vision technology has truly matured? In which technical directions will it evolve in the future? Recently, we invited five companies from different links of the machine vision industry chain - TKH Vision, Teledyne Technologies, as well as industry experts from Qiyuan Vision, Huahan Weiye, and Xinsuan Technology. They conducted in-depth discussions on cutting-edge topics such as current technology maturity, current status and challenges, and future development trends.

Due to the length of this article, this special topic is presented in two parts. In the first part, we will discuss such issues from the perspectives of experts from two foreign companies, TKH Vision and Teledyne Technologies. In the second part, we will present different views from the perspectives of three domestic companies, Qiyuan Vision, Huahan Weiye, and Xinsuan Technology.

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The technology system is becoming stable, but the underlying innovation is still accelerating

When asked "whether the current machine vision technology has matured", the two interviewees gave almost the same answer: machine vision has entered the application maturity stage, but it is still evolving. Specifically, the image acquisition equipment is highly standardized, the algorithm platform is stable, and the deployment cost is reduced. The entire industry has the ability to scale up, but it still maintains a momentum of continuous exploration and development in the integration of software development, cognitive intelligence and AI technology.

Henning Tiarks, Chief Product Officer of TKH Vision, recognized the sustainability of technological development from the perspective of machine vision system development. He believes that not only the performance of components such as camera speed and resolution is constantly improving, but AI software development technology is also in continuous progress. At the same time, standardization, miniaturization and cost optimization allow this technology to penetrate more application scenarios. "This shows that the technical system is becoming stable, which means that the industrial foundation has been laid.

Peng Chuanbao, business development manager of Teledyne DALSA Industrial Vision System, also affirmed the high maturity of machine vision at the hardware level, but for software, there are relatively few software on the market that can truly meet AI, 3D and traditional algorithms and are easy to use.

It can be seen that the current technical system of machine vision has become stable, the infrastructure has matured, and the core components (such as cameras, light sources, lenses, and image acquisition cards) have achieved modularization and standardization, making system integration more efficient and replicable. However, its application scenarios, algorithm capabilities, and system integration methods are still in rapid iteration.

Although it is difficult to generalize the maturity of machine vision technology as mature or approaching maturity, it is undeniable that new technological breakthroughs are still emerging in the field of machine vision to meet more stringent and difficult scene requirements. "Technical breakthroughs in interfaces are particularly obvious. New technologies with high speed and real-time capabilities are emerging rapidly. At present, cameras and image acquisition cards have achieved transmission speeds of 50 to 100 Gbit/s, enabling them to meet the extreme requirements of high-demand applications such as semiconductors and electronics. "Henning Tiarks gave an example.

 

System integration capabilities: the upgraded role of hardware manufacturers in the AI era

Wih the introduction of AI big models, the development of multimodal perception, and the popularization of edge computing, machine vision is undergoing a profound paradigm shift. Although AI big models have shown great potential in image generation, defect recognition, and multimodal understanding, their implementation process is not smooth.

The integration of AI big models and machine vision is not a natural technology superposition. "Especially in the manufacturing industry, which emphasizes real-time, stability, and security, it is difficult to implement AI big models. "Peng Chuanbao said.

The penetration of AI big models has brought new variables to machine vision and put forward new requirements for the role of hardware manufacturers. In the past, the core competitiveness of hardware manufacturers focused on the design and manufacturing of physical layers such as sensors, cameras, and image acquisition cards; now, system integration capabilities have become the key to determining the success or failure of technology implementation.

Faced with the surge in computing power demand and scene fragmentation challenges brought by AI big models, hardware manufacturers must break out of their traditional role positioning and transform from a single "component supplier" to a "system solution provider." The core of this transformation is to achieve a precise match between technical capabilities and industry needs through software and hardware collaborative design.

Behind the software and hardware collaboration is actually a complete set of system thinking. Peng Chuanbao took Teledyne Dalsa as an example. It is not only a hardware manufacturer, but also a visual ecosystem provider. AI smart cameras are equipped with sherlock8 AI vision software platform. This advantage of software and hardware collaboration can bring users a more efficient computing experience.

Henning Tiarks believes: "We must regard the machine as a complete system, not a collection of individual components. Therefore, it is very important to choose a partner who can provide system solutions and has experience in distributed systems. ”

These views reveal a core trend: the competition among traditional hardware manufacturers has shifted from a single performance indicator to a system-level capability competition. Specifically, hardware manufacturers need to upgrade their capabilities in all dimensions. In the AI era, whoever can more comprehensively master system integration capabilities, provide a more efficient cloud-edge-end collaborative architecture, and more effectively connect the chip-compilation-scenario full link will be expected to take the lead in the tide of the integration of AI big models and machine vision.

 

ROI considerations: the balance between technical value and commercial value

The value of technology ultimately has to return to business logic. For machine vision, the consideration of ROI (return on investment) is particularly complex because it involves multiple dimensions: technology maturity, industry characteristics, enterprise scale, etc.

Henning Tiarks believes that the key challenge for enterprises to achieve ROI balance lies in the initial engineering development costs and whether the business case can be clearly described. He once again emphasized the importance of systematic thinking, providing users with a complete solution that is easier to develop and integrate, thereby increasing the possibility of commercial implementation.

Peng Chuanbao provided another perspective, that is, the biggest challenge of ROI balance lies in how to solve the compatibility problem of flexible production lines. This requires that the visual solution must have very good solution compatibility; it must be easier to use and save costs; and finally make full use of AI technology to achieve good compatibility.

Although ROI is an important indicator for enterprises to evaluate the landing value of machine vision technology, it is not the only criterion for measuring the value of technology. The views of the interviewed companies all point to a deeper logic: the matching of technology maturity and demand. Matching is the core variable that determines ROI. In the future, as the maturity of machine vision technology increases significantly and "standardization + modularization" becomes popular, the cost structure will also undergo fundamental changes. This will not only help reduce the initial investment cost of enterprises, but also improve the scalability and ease of use of technology, further promote the widespread application of machine vision technology in various industries, and realize the efficient transformation of technology value into commercial value.

 

The future trend is clear, but key bottlenecks still need to be broken

In the past few years, machine vision technology has made significant progress, from hardware equipment performance improvement to software algorithm optimization, to system integration innovation, all of which have laid a solid foundation for the rapid development of the industry.

At present, with the accelerated release of the demand for intelligent upgrading of the global manufacturing industry, the maturity of technologies such as AI large models, 3D imaging, and edge computing has become more and more mature. , machine vision is standing at the key node of the transformation from "perception tool" to "cognitive engine". How will this field evolve in the next 5 to 10 years? What surprises will it bring to the industry?

On the one hand, cross-domain technology integration has injected new vitality into machine vision. The perception ability of machine vision will shift from a single dimension to multi-dimensional collaboration, and the technical boundaries are constantly being broken. Peng Chuanbao believes that with the accelerated penetration of non-visible light technologies such as X-rays, infrared, lidar, and sonar, the application scope of machine vision will be further broadened in the future, such as the application of underwater machine vision in seabed image recognition, and X-ray technology in the medical field and business applications.

On the other hand, from the perspective of technological innovation, the deep integration of artificial intelligence and deep learning is reshaping the future direction of machine vision. Machine vision is transcending the traditional image capture and processing boundaries. It will transform into "intelligent vision" with autonomous learning and intelligent decision-making. Peng Chuanbao pointed out that the progress of AI and 3D algorithms will promote breakthroughs in image processing technology, especially in the recognition of special materials and transparent objects. In the future, the deep integration of 2D, 3D and AI algorithms will significantly improve the intelligence level of machine vision, enabling it to handle more complex scenes and tasks. He believes that AI technology will have a qualitative improvement in the next few years, reducing computing power requirements, automatic training, unsupervised learning, self-generation of bad pictures, etc. Xing Jianfei mentioned that new technologies such as photon computing and quantum image processing will be applied on a large scale to provide more powerful computing power support for machine vision.

The leap of technology will inevitably lead to the reconstruction of application scenarios. From the perspective of the development of technology landing and industry application, the application scope of machine vision technology continues to expand. Henning Tiarks predicts: "In the traditional manufacturing field, as the deployment of cameras increases, machine vision will build a full-link perception network to achieve global optimization of factory operations through real-time closed-loop feedback rather than local detection; in addition, visual technology will become standard for all autonomous mobile devices, and other bands besides visible light (such as infrared, ultraviolet, etc.) will also move from niche to mainstream applications.

In the next 5 to 10 years, machine vision technology will undergo a profound change, which is not only reflected in the rapid progress of technology, but also in its deep penetration into the industry and the coordinated development of the ecosystem. We see that machine vision is growing from a single perception tool to a multi-functional cognitive engine. Multimodal perception fusion and cross-domain technology integration will jointly push this field to new heights.

The future of machine vision is full of infinite possibilities. It will become an extension of human wisdom, helping us better understand the world and solve problems. We look forward to the arrival of an era of visual intelligence where everything is connected, when machine vision will become a core component of the intelligent ecosystem and create a more efficient and intelligent future for mankind.

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