With the potential to solve complex industrial tasks reliably and consistently, machine vision is employed in a variety of applications. These include electronic component analysis, material inspection, and pattern and object recognition, in addition to validating currency, optical characters, and signatures. According to Inkwood Research, the global machine vision market is projected to progress with a CAGR of 5.54% during the forecasted period of 2023 to 2032. Moreover, the market is set to acquire a revenue share of $19069.88 million by 2032.
Machine vision utilizes the latest artificial intelligence (AI) technologies to provide industrial equipment with the ability to see and analyze tasks in avenues like quality control, smart manufacturing, and worker safety.
Aligning with this, let’s explore how machine vision is transforming the operations and functionality of various industries globally –
- Manufacturing – Machine vision in the field of manufacturing can significantly boost product quality and system efficiency. By integrating this technology into production lines, manufacturers can achieve higher throughput, empower staff to focus on high-value tasks and optimize labor costs. A successful example of this synergy can be witnessed in Audi’s collaboration with Intel and Nebbiolo Technologies.
Through the incorporation of predictive analytics and machine learning algorithms in weld inspection and quality-control processes, Audi experienced a notable increase in daily weld analysis, reduced labor expenses, and a shift towards proactive monitoring. This transformation also enabled Audi to prevent issues before they occur, rather than merely reacting to them, thus streamlining the luxury vehicle manufacturer’s production operations for better outcomes.
- Electronics – With the advent of new manufacturing technologies and processes in the semiconductor and electronics industries, the demand for machine vision has surged. Customers are recognizing its pivotal role in maintaining high yields and profitability, both in traditional applications and emerging ones. Some of the developments in this regard include –
- OMRON Corporation (Japan) launched the PCB inspection system VT-S10 Series, featuring an industry-first imaging technique as well as artificial intelligence in order to automate the high-precision inspection process for electronic substrates. This machine vision advancement eliminates the need for specialist skills, streamlining the inspection process for enhanced efficiency and accuracy.
- Cognex In-Sight 2D Machine Vision Systems by Cognex Corporation (United States) has the ability to identify and inspect various components and parts. These industrial-grade, self-contained vision systems amalgamate a collection of advanced vision tools with high-speed image acquisition as well as processing for enhanced efficiency and precision.
- Automotive – Guidance is one of the most crucial tasks for a machine vision system as it helps the robots perform tasks more efficiently. However, in the automotive industry, analyzing the interior of the vehicle’s body becomes challenging since the robots require full access. Here, cameras, as well as other sensors, provide automated systems with the complete image of the automobile. Moreover, the guidance segment, under the application category, is set to grow with a CAGR of 4.82% by 2032. (Source: Inkwood Research)
Market players in the automotive industry are making significant strides in adopting technologically advanced and innovative products for machine vision guidance applications. This momentum is expected to drive market growth during the forecast period.A notable example is Advantech’s (Taiwan) recent launch of the AIIS-3411, an intelligent system with applications across various verticals. Powered by a 10th generation Intel CoreTMi CPU (LGA1200), this all-in-one machine vision IPC supports PoE or USB 3.0 camera interface through four dedicated channels. With its versatility, AIIS-3411 finds extensive use in machine automation applications, including visual guidance robotics, automated guided vehicles (AGV), autonomous mobile robots (AMR), AI and deep learning, among others.The introduction of such cutting-edge technology promises to enhance production efficiency and reduce costs, thus making it a game-changer for the machine vision market.
- Pharmaceuticals – In tightly regulated industries such as pharmaceuticals, machine vision offers constant checks on product contents, labeling, and packaging for quality assurance. Moreover, complying with stringent FDA regulations, pharmaceutical companies are now required to track products by serial number, associating them with specific manufacturing facilities, batch numbers, and dates.Aligning with this, in January 2023, Asensus Surgical (United States) revealed that its surgical robot, the Senhance system, has received the CE mark for an extended range of machine vision capabilities. This regulatory approval expands the Intelligent Surgical Unit’s (ISU) functionalities, granting it broader machine vision capabilities in Europe, Japan, and the United States.Furthermore, ASMV or application-specific machine vision systems, designed to address a specific application, are widely used in the pharmaceutical industry. Accordingly, Symetix, a United States-based pharmaceutical and nutraceutical group, offers Vantyx and OptyxSG systems. While Vantyx is dedicated to high-performance color inspection for tablets in blister packs, OptyxSG systems are used to inspect softgels and tablets.According to Inkwood Research, ASMV is projected to be the fastest-growing hardware component, progressing with a CAGR of 5.78% between 2023 to 2032. As global regulations continue to evolve, machine vision systems are becoming indispensable tools, empowering the pharmaceutical industry to uphold the highest standards of integrity and safety in its products.
As the rapidly-emerging technology continues to advance and integrate with emerging trends like AI and automation, the global machine vision market is poised for significant growth and innovation. Subsequently, this factor is set to revolutionize industrial processes and machine vision applications over the upcoming years.
What are the challenges in adopting machine vision?
While machine vision offers significant advantages, some challenges include the high initial costs of hardware and software, the need for technical expertise to develop and maintain algorithms, and the complexity of integrating machine vision into existing manufacturing systems.
How is machine vision different from computer vision?
Although machine vision and computer vision are related fields, they have different focuses. While machine vision, a subset of computer vision, is specifically geared toward industrial applications and automation, computer vision has a broader scope and aims to enable computers to understand and interpret visual information in general.