市場調查報告書
商品編碼
1284081
到2028年的機器視覺相機市場預測-按系統,類型,部署,相機類型傳感器,組件,像素,鏡頭,光譜,應用,用戶和地區進行的全球分析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 |
根據 Stratistics MRC 的數據,2022 年全球機器視覺相機市場規模將達到 120 億美元,預計到 2028 年將達到 200 億美元,預測期內的複合年增長率為 9.4%。生長。
機器視覺相機使用帶有專用光學器件的數字傳感器捕捉圖像,然後通過計算機硬件和軟件對圖像進行處理,分析和測量,以產生準確的結果。 機器視覺相機在配置了正確的分辨率和光學器件後,可以輕鬆檢查人眼看不到的微小物體細節。
根據最近的市場研究,機器視覺相機預計將有超過 26% 的收入來自計量和測量應用。
由人工智能驅動的機器視覺系統可以快速識別和對比變化較大的缺陷。 製造設施正在使用基於人工智能的解決方案,通過最大限度地提高資產利用率,減少停機時間和提高機器效率來提高生產率。 基於人工智能的解決方案還有望通過檢測缺陷和支持工廠設備的預測性維護來通過質量控制提高生產率。 此外,基於 AI 的系統可以回顧過去並從中學習,在現在採取行動並預測未來。 因此,機器視覺中對人工智能的需求為該行業帶來了一些高增長前景。
企業主應就基於 AI 的設備的技術能力對員工進行培訓,因為大多數人都不熟悉這項技術的工作原理。 機器視覺技術正在通過基於 AI 的解決方案迅速改變和改進。 伴隨著這種快速發展的技術而來的是不斷增加的培訓成本和持續時間。 此外,培訓不當會導致機器視覺系統編程不當和誤報。 隨著機器視覺技術的日新月異,這些問題使得市場難以擴大。
視覺引導機器人系統應用於自動化任務,例如消費電子產品製造商的質量控制,產品測量,理想放置和預測性維護。 視覺引導機器人可以在沒有安全屏障的情況下避免碰撞,使它們能夠在共享辦公室中與人類一起安全工作。 使用工業機器人的自動化在汽車和消費電子行業發展迅速。 因此,對集成機器視覺系統與視覺引導機器人控制器的需求不斷增長。
數據駭客攻擊和帳戶黑客攻擊是影響工業機器人行為的兩個嚴重問題。 採用人工智能機器視覺和計算機視覺等尖端技術將對該領域產生直接影響。 基於人工智能 (AI) 的機器視覺系統容易受到網路攻擊,這會降低其有效性。 針對它們的網路攻擊可能會損害它們的準確性,安全性和完整性,降低它們的有效性,並由於其製造過程中的缺陷而導致市場價值下降。 因此,對工業機械機器人和小工具進行網絡攻擊的可能性是減緩市場擴張的一個抑制因素。
面對 COVID-19,世界各地的行業都承諾增加對自動化的投資。 此外,隨著公司開始了解自動化質量保證在其製造過程中的價值,需求也在增長。 COVID-19 的爆發減少了人類對許多任務的參與,並增加了全球對機器視覺相機的需求。 因此,機器視覺作為自動化長期發展的一個組成部分而廣為人知。 機器視覺可以快速識別自動化製造過程中的問題。 結果是成本更低,反應時間更快。
估計軟件行業將有良好的增長。 生產線和相機接口由我們的工業機器視覺軟件系列中的軟件工具提供。 程序員可以通過使用帶有 Linux 操作系統的智能相機來提高視覺系統的效率,同時控製成本。 因此,領先的公司正專注於創建開放系統智能相機,允許系統集成商整合來自第三方或開源軟件提供商的所需應用軟件。
在預測期內,汽車行業預計將以最快的複合年增長率增長。 隨著自動駕駛汽車的出現和生產設備本身的自動化,汽車行業正在經歷快速轉型。 由於技術的不斷改進,機器視覺相機的使用正在擴大,例如停車相機,用於側視的 CMS 相機和用於 360 度車輛環繞的 SVS 相機。 機器視覺相機市場預計將受到 ADAS 和自動駕駛汽車在全球範圍內普及的推動。 此外,機器視覺相機用於新零件開發期間和汽車製造過程檢查階段的測量。 這些程序使用線掃描相機,3D 成像相機和條形碼掃描儀等設備。
預計在預測期內,亞太地區將佔據最大的市場份額。 這種巨大的市場份額和區域擴張可歸因於其在亞太地區汽車,包裝,製藥和其他工業應用領域的有利潛力。 隨著該地區發展成為全球製造中心,預計該技術將在預期期間取得顯著進步。 中國和日本是重要的國家,為機器視覺等先進和成熟技術提供了一系列選擇。 當地經濟的增長和繁榮是由各個工業部門推動的。
由於半導體行業(機器視覺系統的主要市場)的主導地位,預計北美在預測期內的複合年增長率最高。 MV 技術也變得更小,更智能,以便集成到自動駕駛汽車,人工智能驅動的揀選和檢測技術改進等自動化應用中。 所有這些都有望推動該領域對中壓系統的需求。
2021 年 3 月,康耐視發布了最新一代便攜式條碼讀取器 DataMan 8700 系列。 該小工具在性能方面是最先進的,並且非常易於操作,無需事先進行微調或操作員培訓。
2021 年 3 月,康耐視宣布推出康耐視邊緣智能 (EI),它使用條碼掃描性能監控和設備管理來幫助客戶避免停機並簡化製造和運輸操作。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.