The global machine vision market size is expected to reach a valuation of USD 24,604 million by 2030, growing at a CAGR of 7.10% during the forecast period (2022–2030). Machine vision points to the methods and technology utilized in industry to provide imaging-based analysis and automatic inspection for applications such as robot navigation, process control, and automated inspection. Machine vision encompasses various technologies, skills, methods, activities, integrated systems, software, and hardware.
As a system engineering subject, machine vision differs from computer vision, a branch of computer science. It tries to combine current technologies in novel ways and apply them to real-world challenges. The phrase is commonly used for these functions in industrial automation applications, but it is also used for vehicle guidance in other environments.
The machine vision process entails evaluating the needs and projects in-depth and finding an optimal solution. While run-time, the process begins with imaging, then moves on to automated picture analysis and information retrieval.
Following COVID-19, manufacturing companies worldwide vow to spend more on automation. Additionally, as firms have grasped the necessity of automated quality assurance in production processes, the need for it has grown. The COVID-19 outbreak has decreased human interference in many functions and has exacerbated this need. Thus, machine vision is now widely accepted as an essential component of long-term automation project development. Machine vision can assist uncover faults in automated manufacturing operations in a short period.
Thus, prices are reduced, and reaction times are improved. It also aids in detecting defective items, lowering the risk of product returns, and increasing consumer satisfaction. With this technology, all items on the manufacturing lines are checked equally and with the same concentration, which has boosted the demand for machine vision in quality assurance. Also, the products are reworked if any defects are discovered—this is the most expensive factor. As per Forbes, using machine learning to automate quality testing can raise the problem identification rate by 90%.
Machine vision has recently seen substantial changes due to vision-guided robotics systems. In the automotive and consumer electronics industries, the usage of industrial robots for automation has increased intensely. Thus, there is a growing demand for machine vision systems to be integrated with vision-guided robot controllers. Machine vision systems improve their efficiency by allowing robots to view and adapt to their surroundings.
For example, when multiple automobile models are produced on a single assembly line, vision-guided robotics systems can detect the scenario and assure appropriate vehicle assembly. A machine vision system can verify no gaps when a robot is spraying adhesive beading. A vision-guided robot can work alongside individuals in a shared workspace without a safety barrier, securely avoiding any collisions.
A machine vision system enables speedy, definite, and specific control with effective manufacturing and better delivery reliability. Because the functionality of these systems is dependent on their upkeep, these machines and devices frequently require close attention and daily maintenance to maintain their reliability. Because this equipment is modern and well-equipped with automation, the maintenance investment is substantial. Installation and purchasing charges are included in these expenditures.
In addition, the loss of qualified individuals is a stumbling block in this sector. Since there is a scarcity of professionals in the industry, employees and operators are given training. Similarly, inspecting numerous products on the same smart camera can be challenging. As a result, skilled people are required. The execution of a machine vision system necessitates specialized and unique equipment knowledge. However, due to increased penetration in various industries, this limitation is expected to be alleviated in the forecast term.
In general, industries are expanding, mainly focusing on advanced technological development. Industry 4.0 is an innovation helping to revolutionize the manufacturing sector. Similarly, the Industrial Internet of Things enhances efficiency, improves quality, reduces waste, increases production efficiency, lowers operating costs, and provides additional advantages on the production line.
This advancement has encouraged the adoption of Cyber-Physical Systems (CPS), which collect data for manufacturers to assist them in recognizing and locating parts and sub-assemblies. Additionally, this data collection method allows devices to communicate, operate, and engage autonomously. The vast amount of data collected by vision equipment will be used as data analytics capabilities advance in the market.
This will aid in detecting and identifying defective items and quick and effective interventions in Industry 4.0, making machine vision a critical aspect of automated processes for production and manufacturing, creating lucrative growth opportunities over the forecast period.
Deep learning combines the benefits of classic rule-based machine vision systems with the judgment of human inspectors but in a semi-automated manner that necessitates ongoing optimization. Machine vision is expanding into new industrial uses because of this technology. Deep learning is highly suited for machine vision analysts and installers in scenarios where subjective judgments must be made, akin to human inspection, especially when identifying challenging features due to the image's intricacy or instability.
Manufacturing activities were impeded by the COVID-19 outbreak, which hampered the growth of the machine vision market. The COVID-19 outbreak prompted tight lockdown restrictions, leading manufacturing and sales operations to be disrupted. The global coronavirus outbreak has significantly influenced manufacturing industries, particularly machine vision market growth. Concerns about the environment and regulations compel firms to think creatively to increase sales and revenue.
This industry has already shown signs of decline in the fourth quarter of 2019 and the first quarter of 2020. Furthermore, the companies anticipate a significantly more significant impact from the pandemic in the third and fourth quarters of 2020 and the first quarter of 2021. According to the producer and distributor, customers do not expect the products due to stock and distribution network concerns. Nevertheless, most businesses expect income to remain below before levels by the end of 2021 and even one year after the crisis's peak.
The post-pandemic period will be crucial for the global machine vision market. The market's growth is likely to be hampered due to a lack of skilled professionals working with advanced technologies. On the other hand, factors like the growing demand for quality and automation inspection will likely continue to drive the market growth over the forecast period.
The global machine vision market share has been classified based on offering, product, application, end-use industry, and regions.
The machine vision market has been segmented into hardware, software, and services based on offerings. The hardware offering segment is expected to dominate the global market. It is projected to reach USD 14,784 million by 2030, registering a CAGR of 6.60% during the forecast period due to the high adoption of the machine learning application. The hardware segment comprises a processor, LED lighting, optics/lenses, frame grabber, and camera. The camera segment is expected to reach USD 4,420, with a CAGR of 4.60% by 2030 due to surging demand for CMOS imaging sensors.
The machine vision market has been segmented into PC-based, smart camera-based products. The PC-based product segment is expected to dominate the global market, and it is projected to reach USD 12,974 million by 2030, registering a CAGR of 6.60% during the forecast period. Over the forecasted period, the segment is predicted to grow and lead the market.
The machine vision market has been segmented into quality and inspection, positioning and guidance, measurement, and identification based on application. The quality and inspection application segment is expected to dominate the global market, and it is projected to reach USD 12,228 million by 2030, registering a CAGR of 6.60% during the forecast period. The systems are widely used in the packaging market for reading and recognizing text, labels, and barcodes. This saves time, removes human errors, and progresses efficiency by automating packaging tasks. Technology use in various industries has significantly reduced counterfeit items, allowing the broader market to acquire impetus for greater adoption.
The machine vision market has been segmented into automotive, pharmaceutical and chemicals, electronics and semiconductors, pulp and paper, printing and labeling, food and beverage, glass and metal, postal and logistics, and others based on end-use industry. The automotive end-use industry segment is expected to dominate the global market, and it is projected to reach USD 4,987 million by 2030, registering a CAGR of 7.60% during the forecast period.
Machine vision is widely utilized in the automotive sector for inspections such as existence checks, error proofing, assembling validation, and final inspection. The machine vision system is employed for dimensional gauging, robotic guidance, and testing automation, all of which fall under the measurement, gauge, and guide category. As a result, demand for automated imaging is high across the automotive industry, and it is expected to expand significantly in the future years.
The global machine vision market share has been segregated into North America, Europe, Asia-Pacific, South America, and the Middle East and Africa.
With a market value of USD 11,429 million by 2030, registering a CAGR of 8.60%, Asia-Pacific is expected to be the most prominent machine vision market. The innovation is expected to gain substantial traction during the projected period as the region establishes itself as a global manufacturing base. Japan and China are two important countries with the capacity to provide vast potential for both new and mature technology like machine vision. Many manufacturing sectors contribute to the expansion and prosperity of the area's economy.
Moreover, the spending and operational efficiencies, combined with government schemes in developing countries like Singapore, India, Taiwan, and South Korea, are willing to take responsibility for flinging investments and encourage diverse industrial players to set up the manufacturing units in Asia-Pacific.
Europe is expected to be the second-largest machine vision market, with a value of USD 5,074 million by 2030, registering a CAGR of 4.70% during the forecast period. Europe will experience significant growth in the future. The extraordinary growth in automation and technology is reliant on machine vision systems. The demand for quality checks and automation inside industrial units is also projected to be a significant driver of the growing market in the region.