In 2025, manufacturers in the United Kingdom and the European Union are expected to lose over £80 billion due to unplanned production downtime. The scale of these losses highlights how dangerous unpredictability in equipment operation and technological processes can be for businesses. Traditional maintenance methods often fail to respond in time, as problems become apparent only after causing damage, so, companies increasingly turn to digital solutions and Computer Vision that allow them to anticipate and solve potential issues in advance.
Mechanical systems have a limited service life, and gradual wear of components is often imperceptible to the human eye. Recent studies show that combining different computer vision methods significantly improves defect detection accuracy. For example, in 2024, a research team proposed a system for analyzing images of milling tools using two new descriptors, one describing the shape of the worn area and the other its contour. In a two-class classification (“high/low wear”), the accuracy reached about 91%, and in a three-level classification (“high/medium /low wear”), it exceeded 82%.
This case demonstrates that computer vision can distinguish even minor deviations in the shape and edges of damaged areas, which are not always noticeable to the naked eye or standard sensors.
CV systems can also monitor correct component assembly, coating accuracy, component placement on the conveyor, and more. This allows problems to be detected at early stages, when resolving them does not require significant time and resources. Additionally, integrating CV with analytical algorithms enables automatic process adjustments, product rerouting, or alerts to operators about critical deviations.
CV is actively applied to monitor workflow and safety standards on production lines. For example, systems can detect in real time the absence or improper use of personal protective equipment (PPE), such as helmets, vests, gloves, and goggles.
According to a study published in October 2024, a system using the YOLOv8 model accurately detected helmets on construction sites.
The effectiveness of such solutions directly depends on high-quality data for training models. Companies like Keymakr help improve recognition accuracy even in demanding production environments.
With growing order volumes, the need for fast goods handling is increasing. Traditional management methods often do not provide the required efficiency, and even minor delays can lead to significant financial losses. Computer vision becomes a powerful tool for optimizing warehouse processes, allowing companies to predict the consequences of changes in product placement or personnel routes.
Last year, Amazon Web Services (AWS) introduced a Warehouse Automation and Optimization (WAO) service, which includes creating digital warehouse twins using AWS IoT TwinMaker. This allows simulating different scenarios, optimizing layouts and processes, and integrating robotics and automated systems with AWS RoboMaker and AWS IoT Greengrass.
These innovations significantly increase operational efficiency, reduce costs, and improve customer service. Optimizing “pick-and-put-away” processes can increase labor productivity by 40%, while 3D visualization and digital twins can improve space utilization by 15%.
In the coming years, computer vision in manufacturing will continue to grow, becoming an integral part of modern smart factories aligned with the concept of Industry 4.0. Industry 4.0 involves implementing digital technologies, automation, the Internet of Things, and AI to create highly efficient, adaptive, and self-learning production. Computer vision systems will become more integrated with big data analytics and AI algorithms, enabling the detection of defects and wear and the prediction of potential issues before they occur.
In 2025, Hyundai Motor Group opened the new Metaplant America factory in Georgia, USA. It is the largest car plant in the U.S., built from scratch using artificial intelligence, robotics, and digital twin technologies. The factory has over 23 AI and robotic systems, including drones and inspection robots. Total project investments amount to $7.6 billion.
In the near future, CV systems will be able to track many more parameters simultaneously and work in conjunction with digital twins. This will allow for risk-free testing of production scenarios and faster response to changes.
