市场调查报告书
商品编码
1284081
到2028年的机器视觉相机市场预测-按系统,类型,部署,相机类型传感器,组件,像素,镜头,光谱,应用,用户和地区进行的全球分析Machine Vision Camera Market Forecasts to 2028 - Global Analysis By System, Type, Deployment, Camera Type Sensor Type, Component Pixel Type, Lens Type, Spectrum Type, Application, End User and Geography |
根据 Stratistics MRC 的数据,2022 年全球机器视觉相机市场规模将达到 120 亿美元,预计到 2028 年将达到 200 亿美元,预测期内的复合年增长率为 9.4%。生长。
机器视觉相机使用带有专用光学器件的数字传感器捕捉图像,然后通过计算机硬件和软件对图像进行处理,分析和测量,以产生准确的结果。 机器视觉相机在配置了正确的分辨率和光学器件后,可以轻鬆检查人眼看不到的微小物体细节。
根据最近的市场研究,机器视觉相机预计将有超过 26% 的收入来自计量和测量应用。
由人工智能驱动的机器视觉系统可以快速识别和对比变化较大的缺陷。 製造设施正在使用基于人工智能的解决方案,通过最大限度地提高资产利用率,减少停机时间和提高机器效率来提高生产率。 基于人工智能的解决方案还有望通过检测缺陷和支持工厂设备的预测性维护来通过质量控制提高生产率。 此外,基于 AI 的系统可以回顾过去并从中学习,在现在采取行动并预测未来。 因此,机器视觉中对人工智能的需求为该行业带来了一些高增长前景。
企业主应就基于 AI 的设备的技术能力对员工进行培训,因为大多数人都不熟悉这项技术的工作原理。 机器视觉技术正在通过基于 AI 的解决方案迅速改变和改进。 伴随着这种快速发展的技术而来的是不断增加的培训成本和持续时间。 此外,培训不当会导致机器视觉系统编程不当和误报。 随着机器视觉技术的日新月异,这些问题使得市场难以扩大。
视觉引导机器人系统应用于自动化任务,例如消费电子产品製造商的质量控制,产品测量,理想放置和预测性维护。 视觉引导机器人可以在没有安全屏障的情况下避免碰撞,使它们能够在共享办公室中与人类一起安全工作。 使用工业机器人的自动化在汽车和消费电子行业发展迅速。 因此,对集成机器视觉系统与视觉引导机器人控制器的需求不断增长。
数据骇客攻击和帐户黑客攻击是影响工业机器人行为的两个严重问题。 采用人工智能机器视觉和计算机视觉等尖端技术将对该领域产生直接影响。 基于人工智能 (AI) 的机器视觉系统容易受到网路攻击,这会降低其有效性。 针对它们的网路攻击可能会损害它们的准确性,安全性和完整性,降低它们的有效性,并由于其製造过程中的缺陷而导致市场价值下降。 因此,对工业机械机器人和小工具进行网络攻击的可能性是减缓市场扩张的一个抑制因素。
面对 COVID-19,世界各地的行业都承诺增加对自动化的投资。 此外,随着公司开始了解自动化质量保证在其製造过程中的价值,需求也在增长。 COVID-19 的爆发减少了人类对许多任务的参与,并增加了全球对机器视觉相机的需求。 因此,机器视觉作为自动化长期发展的一个组成部分而广为人知。 机器视觉可以快速识别自动化製造过程中的问题。 结果是成本更低,反应时间更快。
估计软件行业将有良好的增长。 生产线和相机接口由我们的工业机器视觉软件系列中的软件工具提供。 程序员可以通过使用带有 Linux 操作系统的智能相机来提高视觉系统的效率,同时控製成本。 因此,领先的公司正专注于创建开放系统智能相机,允许系统集成商整合来自第三方或开源软件提供商的所需应用软件。
在预测期内,汽车行业预计将以最快的复合年增长率增长。 随着自动驾驶汽车的出现和生产设备本身的自动化,汽车行业正在经历快速转型。 由于技术的不断改进,机器视觉相机的使用正在扩大,例如停车相机,用于侧视的 CMS 相机和用于 360 度车辆环绕的 SVS 相机。 机器视觉相机市场预计将受到 ADAS 和自动驾驶汽车在全球范围内普及的推动。 此外,机器视觉相机用于新零件开发期间和汽车製造过程检查阶段的测量。 这些程序使用线扫描相机,3D 成像相机和条形码扫描仪等设备。
预计在预测期内,亚太地区将占据最大的市场份额。 这种巨大的市场份额和区域扩张可归因于其在亚太地区汽车,包装,製药和其他工业应用领域的有利潜力。 随着该地区发展成为全球製造中心,预计该技术将在预期期间取得显着进步。 中国和日本是重要的国家,为机器视觉等先进和成熟技术提供了一系列选择。 当地经济的增长和繁荣是由各个工业部门推动的。
由于半导体行业(机器视觉系统的主要市场)的主导地位,预计北美在预测期内的复合年增长率最高。 MV 技术也变得更小,更智能,以便集成到自动驾驶汽车,人工智能驱动的拣选和检测技术改进等自动化应用中。 所有这些都有望推动该领域对中压系统的需求。
2021 年 3 月,康耐视发布了最新一代便携式条码读取器 DataMan 8700 系列。 该小工具在性能方面是最先进的,并且非常易于操作,无需事先进行微调或操作员培训。
2021 年 3 月,康耐视宣布推出康耐视边缘智能 (EI),它使用条码扫描性能监控和设备管理来帮助客户避免停机并简化製造和运输操作。底部。
According to Stratistics MRC, the Global Machine Vision Camera Market is accounted for $12 billion in 2022 and is expected to reach $20 billion by 2028 growing at a CAGR of 9.4% during the forecast period. Digital sensors with specialised optics are used by machine vision cameras to capture images, which are then processed, analysed, and measured by computer hardware and software to produce accurate results. A machine vision camera can easily examine minute object details that are too small to be seen by the human eye if it is built around the right resolution and optics.
According to recent market research, Machine vision cameras are projected to generate over 26% of their revenue from gauging and measurement applications.
Machine vision systems powered by artificial intelligence are capable of quickly identifying and contrasting flaws with significant variability. Manufacturing facilities use AI-based solutions to increase productivity by maximising asset utilisation, reducing downtime, and improving machine efficiency. It is also anticipated that AI-based solutions will increase productivity through quality control by detecting flaws and assisting in the predictive maintenance of factory equipment. Additionally, AI-based systems are able to look back on the past and learn from it, act in the present, and predict the future. As a result, the industry will have several high-growth prospects thanks to the need for AI in machine vision.
Since most people are not familiar with how this technology works, business owners must train their staff in the technical capabilities of AI-based devices. Machine vision technology is rapidly changing and improving with AI-based solutions. The cost and length of training have increased as a result of these quickly evolving technologies. Inadequate training can also lead to poor programming of machine vision systems, which can lead to erroneous findings. These problems make it difficult for the market to expand since machine vision technology changes quickly.
In order to automate quality control, product measurement, ideal placement, and predictive maintenance tasks for consumer electronics manufacturers, vision guided robots systems must be used. Even without a safety barrier, a vision-guided robot can safely operate alongside people in a shared office because it can prevent collisions. The usage of industrial robots for automation in the automotive and consumer electronics industries has rapidly increased. The demand to integrate machine vision systems with vision-guided robot controllers is growing as a result of this.
Data hacking and account hacking are two serious concerns that can affect how well industrial robots work. The adoption of cutting-edge technologies in this area, such as AI machine vision and computer vision, will be directly impacted by this. Artificial intelligence (AI)-based machine vision systems are vulnerable to cyber attacks, which can reduce their effectiveness. We may encounter cyber attacks against them that compromise accuracy, safety, and integrity, which can reduce their effectiveness and result in a decline in market value due to manufacturing process flaws. Because of this, the potential of cyber attacks on industrial machine robots and gadgets is a restraint that is slowing market expansion.
Ahead of COVID-19, industrial firms all over the world have committed to increasing their investments in automation. Additionally, as companies have come to understand the value of automated quality assurance in manufacturing processes, demand has grown. The COVID-19 outbreak has increased demand for machine vision cameras globally by decreasing human engagement in numerous operations. As a result, machine vision is now widely seen as being a crucial part of the long-term development of automation. Machine vision can quickly identify problems in automated manufacturing processes. Costs are reduced as a consequence, and reaction times are quicker.
The Software segment is estimated to have a lucrative growth. An interface for production lines and cameras is provided by software tools in a collection of industrial machine vision software. The programmer can increase the efficiency of a vision system at a reduced cost by using smart cameras with Linux OS. Major corporations are concentrating on creating open system smart cameras as a result, allowing system integrators to incorporate the required application software from either a third-party or open-source software provider.
The automotive segment is anticipated to witness the fastest CAGR growth during the forecast period. With the advent of the autonomous car and automation within the production facility itself, the automobile industry is undergoing a fast transformation. The use of machine vision cameras, such as parking cameras, CMS cameras for side views, and SVS cameras for a 360-degree view surrounding the automobile, has grown as a result of ongoing technical improvement. The market for machine vision cameras is anticipated to be driven by the growing use of ADAS and autonomous cars worldwide. Additionally, the machine vision cameras are used for measurement during the development of new parts and in the inspection phase of the automotive manufacturing process. These programmes make use of line scan cameras, 3D imaging cameras, barcode scanners, and other devices.
Asia Pacific is projected to hold the largest market share during the forecast period. This enormous market share and regional expansion may be responsible for the lucrative potential in the automotive, packaging, pharmaceutical, and other industrial applications in the Asia Pacific region. As the region develops itself as a hub for global manufacturing, the technology is anticipated to gain significant pace throughout the anticipated timeframe. Two important countries with the ability to provide a variety of options for both advancing and established technologies like machine vision are China and Japan. The growth and prosperity of the local economy are facilitated by a variety of industrial sectors.
North America is projected to have the highest CAGR over the forecast period, owing to the dominance of the region's main market for Machine Vision systems, the semiconductor sector. In order to integrate into automation applications like autonomous cars, AI-driven been picking, improved inspection technologies, and so forth, MV technologies are also becoming smaller and smarter. All of this is anticipated to increase demand for MV systems in the area.
Some of the key players profiled in the Machine Vision Camera Market include Qualcomm Technologies, Hexagon AB, LMI Technologies, Toshiba Teli, Cognex, Nikon, USS Vision, National Instruments Corporation, Sony Corp. and Teledyne DALSA Inc.
In March 2021, Cognex introduced the DataMan 8700 Series, the latest generation of portable barcode readers. The gadget is cutting-edge in terms of performance and is extremely simple to operate, requiring no prior tweaking or operator training.
In March 2021, Cognex released Cognex Edge Intelligence (EI) which uses barcode scanning performance monitoring and device management to assist clients in avoiding downtime and enhance the efficiency of manufacturing and shipping operations.
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.