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市场调查报告书
商品编码
1370785
电脑视觉市场 - 2018-2028 年全球产业规模、份额、趋势、机会和预测,按组件、产品类型、按应用、垂直领域、地区和竞争细分Computer Vision Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, 2018-2028F Segmented By Component, By Product Type, By Application, By Vertical, By Region and Competition |
预计全球电脑视觉市场在预测期内将以健康的复合年增长率成长。电脑视觉是人工智慧的一个分支,它使电脑能够看到、理解和处理视觉讯息。电脑视觉是一种多功能技术,可应用于医疗保健、製造和零售等许多领域。例如,它可用于识别和验证图像或影片中的人脸。这可用于安全目的,例如用于存取控制的脸部辨识或社交媒体应用程序,例如在照片中标记朋友。电脑视觉可以帮助自动驾驶车辆感知周围环境,侦测障碍物、交通标誌、行人和其他车辆,并安全且有效率地导航。电脑视觉可以帮助医生和放射科医生诊断疾病、检测肿瘤、测量器官和组织以及进行手术。它可以用图形、声音、文字和视讯等数位资讯增强现实世界,可用于游戏、教育、旅游等。电脑视觉可以使机器人和机器执行需要视觉检查的任务,例如品质控制、缺陷检测、分类和包装。电脑视觉是一个具有挑战性的领域,需要解决许多复杂的问题。最具挑战性的问题之一是从相机或感测器捕获高品质影像或影片。这是因为影像或视讯可能会受到光照、杂讯、失真和遮蔽等因素的影响。电脑视觉系统需要对影像或影片进行预处理,以提高其品质、减小其尺寸并提取有用的特征以进行进一步分析。电脑视觉系统需要使用各种方法来解释图像或视频,例如分割、分类、检测、识别和追踪。电脑视觉系统需要使用场景理解、物件辨识、脸部辨识和自然语言处理等技术来理解图像或影片的含义和上下文。电脑视觉是一个快速发展的领域,依赖许多技术和工具,例如机器学习、深度学习和影像处理。深度学习是机器学习的子集,它使用人工神经网路从大量资料中学习并执行复杂的任务。深度学习已广泛应用于影像分类、目标侦测和人脸辨识等电脑视觉任务。 OpenCV 是一个开源软体,为电脑视觉提供了一整套功能和演算法。 OpenCV支援C++、Python、Java等多种程式语言,可以运行在Windows、Linux、Android等多种平台上。 TensorFlow 是一个开源平台,为建构和部署机器学习模型提供了平台。 TensorFlow支援Python、C++等多种程式语言,可以运作在CPU、GPU、TPU等多种装置上。电脑视觉是一个令人着迷且重要的领域,为社会和人类带来许多好处。然而,电脑视觉也带来了一些需要仔细解决的伦理和社会问题。例如,电脑视觉可以在未经人们同意或不知情的情况下捕捉人们的脸部、位置、活动和偏好,从而侵犯人们的隐私。这可能导致身份盗窃、监视、滥用或歧视。计算机视觉可能会因其训练资料或使用的演算法而产生偏差。这可能会导致某些人群因性别、种族或年龄而产生不公平或不准确的结果。电脑视觉可以对人们的生活和福祉产生重大影响。
电脑视觉是人工智慧 (AI) 的一个领域,它使电脑和系统能够从数位影像、视讯和其他视觉输入中获取有意义的信息,并根据该信息采取行动或提出建议。电脑视觉最有前途和创新的应用之一是机器人技术。机器人可以使用电脑视觉来感知周围环境、识别物体、自主导航、操纵物品并执行复杂的任务。视觉引导系统是一种电脑视觉技术,允许机器人使用摄影机和感测器作为输入与其环境进行互动。视觉引导系统可以分为两类:2D 和 3D。 2D 视觉引导系统使用传统相机捕捉场景影像,并使用演算法对其进行处理以检测特征、边缘、形状、颜色等。2D 视觉引导系统适用于需要简单物件识别和对齐的任务,例如拾取物品并将其放置在传送带上。 3D 视觉引导系统使用立体相机、结构光或雷射扫描器来捕捉场景的深度资讯并创建环境的 3D 模型。 3D 视觉引导系统可以处理需要精确的物件侦测、定位、定向和姿态估计的更复杂的任务,例如组装零件或拆垛盒子。
市场概况 | |
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预测期 | 2024-2028 |
2022 年市场规模 | 156.5亿美元 |
2028 年市场规模 | 427.9亿美元 |
2023-2028 年复合年增长率 | 18.21% |
成长最快的细分市场 | 软体 |
最大的市场 | 北美洲 |
3D 视觉引导系统的一个例子是 KEYENCE 的 3D 视觉引导机器人系统,该系统专为无与伦比的物体检测能力和易用性而设计。该系统可用于组装、卸垛和机器维护过程的自动化。为了收集 3D资料,当高速投影机在目标上发射多个条纹光图案时,四个相机、一个投影机成像单元总共捕捉 136 个影像。使用者遵循简单的设定流程,包括自动机器人相机校准。
随着电动车变得越来越流行,电脑视觉在电动车的发展中发挥越来越重要的作用。电脑视觉可用于电动车中的各种应用,例如自动驾驶、驾驶员辅助、安全、导航和娱乐。推动电动车电脑视觉需求的主要因素之一是自动驾驶的需求。自动驾驶是指车辆在没有人工干预的情况下运作的能力,使用感测器、摄影机和软体来侦测周围环境并做出反应。自动驾驶可以为电动车带来许多好处,例如减少排放、提高效率、增强安全性以及节省时间和金钱。推动电动车电脑视觉需求的另一个因素是驾驶辅助的需求。驾驶辅助是指利用电脑视觉系统协助驾驶者完成各种任务,例如停车、车道维持、避免碰撞、交通标誌识别和盲点侦测。驾驶员辅助可以帮助提高电动车的性能和安全性,并为驾驶员和乘客提供便利和舒适。推动电动车电脑视觉需求的第三个因素是安全需求。安全是指使用电脑视觉系统来监控和保护车辆及其乘员免受各种危险,例如盗窃、故意破坏、火灾和事故。安全性可以帮助防止或减轻电动车的损坏和伤害,并为车主和使用者提供安心和安全。
总之,电动车需求的不断增长正在推动全球电脑视觉市场的发展。电脑视觉在电动车中有许多应用,可以为使用者和社会带来各种好处。随着技术的进步和消费者偏好的变化,电脑视觉将在塑造移动出行的未来方面发挥越来越重要的作用。
根据组件,市场分为硬体和软体。根据产品类型,市场分为基于智慧相机和基于PC的市场。根据应用,市场进一步分为品质保证和检测、定位和引导、测量、识别、3D 视觉化和互动式 3D 建模以及预测性维护。基于垂直,市场进一步分为工业和非工业。市场分析也研究区域细分,以设计区域市场细分,分为北美、欧洲、亚太地区、南美以及中东和非洲。
Alphabet Inc.、Cognex Corporation、Intel Corporation、Keyence Corporation、Matterport, Inc.、National Instruments Corp.、Omron Corporation、Sony Group Corporation、Teledyne Technologies Inc. 和 Texas Instruments Incorporated。是推动全球电脑视觉市场成长的主要参与者之一。
在本报告中,除了以下详细介绍的产业趋势外,全球电脑视觉市场还分为以下几类:
(註:公司名单可依客户要求客製化。)
Global computer vision market is expected to grow at a healthy CAGR during the forecast period. Computer vision is a branch of artificial intelligence that enables computers to see, understand, and process visual information. Computer vision is a versatile technology with applications in many domains such as healthcare, manufacturing, and retail. For example, it can be used to identify and verify people's faces from images or videos. This can be used for security purposes, such as facial recognition for access control or for social media applications, such as tagging friends in photos. Computer vision can help autonomous vehicles to perceive their surroundings, detect obstacles, traffic signs, pedestrians, and other vehicles, and navigate safely and efficiently. Computer vision can assist doctors and radiologists in diagnosing diseases, detecting tumors, measuring organs and tissues, and performing surgeries. It can enhance the real world with digital information, such as graphics, sounds, texts, and videos, which can be used for gaming, education, tourism, and more. Computer vision can enable robots and machines to perform tasks that require visual inspection, such as quality control, defect detection, sorting, and packaging. Computer vision is a challenging field that requires solving many complex problems. One of the most challenging problems is capturing high-quality images or videos from cameras or sensors. This is because the images or videos can be affected by factors such as lighting, noise, distortion, and occlusion. Computer vision systems need to pre-process the images or videos to enhance their quality, reduce their size, and extract useful features for further analysis. Computer vision systems need to interpret the images or videos using various methods, such as segmentation, classification, detection, recognition, and tracking. Computer vision systems need to understand the meaning and context of the images or videos using techniques such as scene understanding, object recognition, face recognition, and natural language processing. Computer vision is a rapidly evolving field that relies on many technologies and tools, such as machine learning, deep learning, and image processing. Deep learning is a subset of machine learning that uses artificial neural networks to learn from large amounts of data and perform complex tasks. Deep learning has been widely used for computer vision tasks such as image classification, object detection, and face recognition. OpenCV is an open source that provides a comprehensive set of functions and algorithms for computer vision. OpenCV supports various programming languages such as C++, Python, and Java, and can run on various platforms such as Windows, Linux, and Android. TensorFlow is an open source provides a platform for building and deploying machine learning models. TensorFlow supports various programming languages such as Python and C++, and can run on various devices such as CPUs, GPUs, and TPUs. Computer vision is a fascinating and important field that has many benefits for society and humanity. However, computer vision also poses some ethical and social issues that need to be addressed carefully. For example, computer vision can invade people's privacy by capturing their faces, locations, activities, and preferences without their consent or knowledge. This can lead to identity theft, surveillance, abuse, or discrimination. Computer vision can be biased by the data it is trained on or the algorithms it uses. This can result in unfair or inaccurate outcomes for certain groups of people based on their gender, race, or age. Computer vision can have significant impacts on people's lives and well-being.
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs and take actions or make recommendations based on that information. One of the most promising and innovative applications of computer vision is in robotics. Robots can use computer vision to perceive their surroundings, recognize objects, navigate autonomously, manipulate items, and perform complex tasks. Vision-guided systems are a type of computer vision technology that allow robots to interact with their environment using cameras and sensors as inputs. Vision-guided systems can be classified into two categories: 2D and 3D. 2D vision-guided systems use conventional cameras to capture images of the scene and process them using algorithms to detect features, edges, shapes, colors, etc. 2D vision-guided systems are suitable for tasks that require simple object recognition and alignment, such as picking and placing items on a conveyor belt. 3D vision-guided systems use stereo cameras, structured light, or laser scanners to capture depth information of the scene and create a 3D model of the environment. 3D vision-guided systems can handle more complex tasks that require accurate object detection, localization, orientation, and pose estimation, such as assembling parts or de-palletizing boxes.
Market Overview | |
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Forecast Period | 2024-2028 |
Market Size 2022 | USD 15.65 Billion |
Market Size 2028 | USD 42.79 Billion |
CAGR 2023-2028 | 18.21% |
Fastest Growing Segment | Software |
Largest Market | North America |
One example of a 3D vision-guided system is the 3D vision-guided robotics system from KEYENCE, which is designed for unparalleled object detection capability and ease-of-use. This system can be used in the automation of assembly, de-palletizing, and machine tending processes. To gather 3D data, the four-camera, one-projector imaging unit captures 136 total images as the high-speed projector emits multiple striped-light patterns across the target. The user follows a simple setup process, including automatic robot-camera calibration.
Vision-guided systems enable robots to perform tasks that were previously impossible or impractical for humans or machines. They also reduce the need for expensive and time-consuming fixtures, templates, or markers that are used to guide robots in traditional methods. Vision-guided systems can adapt to changes in the environment or the task without requiring manual intervention or reprogramming. Vision-guided systems also improve the quality and consistency of the output by minimizing errors and defects.
As robots become more intelligent and capable with the help of computer vision, they will be able to take on more roles and responsibilities in various domains. This will create new opportunities and challenges for businesses and consumers alike. Vision-guided systems are not only fueling the market for computer vision but also transforming the future of robotics.
As EVs become more popular, computer vision is playing an increasingly important role in their development. Computer vision can be used for a variety of applications in EVs, such as autonomous driving, driver assistance, safety, navigation, and entertainment. One of the main factors that is driving the demand for computer vision in EVs is the need for autonomous driving. Autonomous driving refers to the ability of a vehicle to operate without human intervention, using sensors, cameras, and software to detect and respond to the surrounding environment. Autonomous driving can offer many benefits for EVs, such as reducing emissions, improving efficiency, enhancing safety, and saving time and money. Another factor that is boosting the demand for computer vision in EVs is the need for driver assistance. Driver assistance refers to the use of computer vision systems to assist drivers in various tasks, such as parking, lane keeping, collision avoidance, traffic sign recognition, and blind spot detection. Driver assistance can help improve the performance and safety of EVs, as well as provide convenience and comfort for drivers and passengers. A third factor that is fuelling the demand for computer vision in EVs is the need for safety. Safety refers to the use of computer vision systems to monitor and protect the vehicle and its occupants from various hazards, such as theft, vandalism, fire, and accidents. Safety can help prevent or mitigate damage and injury for EVs, as well as provide peace of mind and security for owners and users.
In conclusion, the rising demand for EVs is driving the global computer vision market. Computer vision has many applications in EVs that can offer various benefits for users and society. As technology advances and consumer preferences change, computer vision will play an increasingly important role in shaping the future of mobility.
Based on components, the market is segmented into hardware and software. Based on product type, the market is segmented into smart camera-based and PC-based. Based on application, the market is further bifurcated into quality assurance & inspection, positioning & guidance, measurement, identification, 3D visualization & interactive 3D modelling, and predictive maintenance. Based on vertical, the market is further split into industrial and non-industrial. The market analysis also studies the regional segmentation to devise regional market segmentation, divided among North America, Europe, Asia-Pacific, South America, and Middle East & Africa.
Alphabet Inc., Cognex Corporation, Intel Corporation, Keyence Corporation, Matterport, Inc., National Instruments Corp., Omron Corporation, Sony Group Corporation, Teledyne Technologies Inc., and Texas Instruments Incorporated. are among the major players that are driving the growth of the global Computer Vision market.
In this report, the global computer vision market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:
(Note: The companies list can be customized based on the client requirements.)