What is Machine Vision?

You might know about Apple Vision Pro in the consumer electronic sector as it brings consumers an immersive VR experience in the digital world, but have you ever heard of Machine Vision in the process of industrial automation? And understand how this innovative technology flourishes across every production line and frees the workforce from the human vision in recent years.

Driven by the advancements in data science, AI algorithm and 5G, machine vision is proven with the ability to enhance the efficiency and accuracy of visual-based quality management in manufacturing, healthcare, automotive, etc. Machine vision employs computer vision, AI algorithms, and analytic patterns to enable machines to interpret, understand, and process the visual information collected from the real world. In other words, compared to human vision combining the collaboration between the brain and eyes, machine vision utilizes the collaboration of cameras, the internet, and AI algorithms to realize accurate, tireless, speedy execution in specific scenarios that require repetition and zero error. Key applications of machine vision include product inspection, defect detection, object identification, and tracking. The global machine vision market is expected to be worth USD 18.4 billion by 2028, growing at a CAGR of 7.3% during the forecast period, according to Markets and Markets.

How does it work and what are the hurdles?

In the landscape of scale economics, the primary challenge is to ensure the consistency of product quality, which depends on strict inspection at every stage of the production line. The other hurdle is to master the ability to detect defectives instantly and react to problems efficiently, so the quality is never compromised.

As a crucial part of automation systems in industry 4.0, leveraging the machine vision to reduce product defectives can help manufacturers to save up to 15%-20% quality-related cost. Serving for the agile and flexible production line, the machine vision system can be deployed in either the PLC system or SCADA system, allowing manufacturers to take out the defectives instantly.

Industrial-grade, high-resolution cameras, laser scanners, sensors, and 3D renderings are the basic elements in collecting data for machine vision recognition. By eliminating the cable limitations, these terminal devices equipped with wireless modules can gather and transfer high-definition feeds (images/videos) to the operational platform in real-time, thus ensuring the processing and analysis of the data. Industrial manufacturers can streamline the decision-making cycle and avoid human errors with the machine vision system.

As the core component in gathering the data for machine vision, the fundamental of industrial-grade camera is to transfer the light signal to digital signal, then process it with analytical ability, which requires the integration of edge compute capability. Compared with consumer-grade camera, the machine vision set higher standards on data throughput, robustness and image formation. The industrial camera not only plays a critical role in affecting image quality and resolution ratio, but is deeply rooted in the operation of machine vision system.

Typical use scenarios of machine vision

Manufacturing

The electronics industry contributes nearly 50% of the demand for machine vision, which is mainly used for wafer cutting, 3C surface inspection, PCB circuit board inspection, electronic packaging, touch screen manufacturing, etc. Taking iPhone as an example, the entire production process requires more than 70 machine vision systems.

Another typical scenario in manufacturing is automotive production. Machine vision is mainly used for precision measurement of component dimensions, surface defect detection, and gap detection. The use of 3D vision systems can help chassis manufacturers to fully automatize the installation process of body panels, thereby ensuring that automated robotic arms detect defects before welding components and reducing rework costs.

Pharmaceutical

The pharmaceutical industry also needs to make full use of machine vision to ensure the quality and safety of drug production. Machine vision is mainly used for medicine bottle packaging defect detection, capsule packaging detection, tablet color identification and sorting, etc. In the detection process, by setting the image sensor, the target image is collected and transferred to the computer via internet, and then compared with the preset area parameters, which can determine defective drugs quickly and accurately.

Food Industry

Machine vision is handy and useful in food industry. For example, a food sorter can use multiple cameras to capture images of product surfaces at the same time. When the shape is set to a circle and the radius parameter is added, food that does not meet the specifications can be automatically filtered out and picked out. In color sorting, by scanning the surface conditions, manufacturers can sort out the unqualified products based on the parameters such as color and proportion.

Fibocom’s edge intelligence solution for machine vision

Standing at the forefront of edge intelligence, Fibocom’s highly integrated 4G/5G smart modules deliver excellent performance and computing power at the edge for industrial cameras, satisfying the image and video process requirements.

Industrial camera incorporated with Fibocom’s smart modules and AidLux’s AI algorithm

The Fibocom’s 5G smart module SC171 can provide up to 12 TOPS edge computing power and 5G connection to the industrial camera, in addition to the integration of AidLux’s Aid-Industry solution, which is a one-stop platform that includes the integration of AI algorithm, training models, and edge deployments. The solution can significantly streamline the implementation of a machine vision inspection system without re-building complex hardware architecture, simplifying the standard operations of the production line adjustments in just a few steps. The platform also utilizes deep learning to extract defective information such as scratches, stains, and dents on the product surface, and identify product information through DM codes, QR codes, and OCR automatically to realize hardware cost-efficiency. It is an ideal edge AI solution that is suitable for more than 80% of industrial inspection scenarios such as the manufacturing of electronic components, medicals, food, and more.

Driven by the advancements in image processing, data science and the maturity of LLM (large language model) training, the applied field of machine vision is expected to expand to a broader range, to ease the labor-intensive workload from repetition and infuse with intelligence and agility, which lead to the emerge of computer vision. How does the enhanced image recognition, object detection, semantic interpretation and augmented reality of computer vision inject new productivity into the industries? Let’s explore in the next blog.