Global Deep Learning In Machine Vision Market is Growing at a Significant Rate in the Forecast Period 2018-2025

Global Deep Learning in Machine Vision Market is expected to reach USD 997.27 million by 2025 and is projected to register a CAGR of healthy rate in the forecast period 2018 to 2025. The new market report contains data for historic years 2016, the base year of calculation is 2017 and the forecast period is 2018 to 2025Access Full Report: http://databridgemarketresearch.com/reports/global-deep-learning-in-machine-vision-market/

Segmentation: Global Deep Learning In Machine Vision Market

Global Deep Learning in Machine Vision Market By Application (Image Classification, Optical Character Recognition, Bar Code Detection, Anomaly Detection), By End-User (Automotive, Electronics, Food & Beverage, Healthcare, Aerospace & Defense, Others), By Geography (North America, Europe, Asia-Pacific, South America, Middle East & Africa) - Industry Trends and Forecast to 2025

Drivers: Global Deep Learning In Machine Vision Market

Some of the major factors driving the market for global deep learning in machine vision market are increasing need for quality inspection and automation, growth in adoption of cloud-based applications and growing demand for vision-guided robotic systems.  

INCREASING NEED FOR QUALITY INSPECTION AND AUTOMATION

  • The increasing applications of deep learning in machine vision market for quality inspection and automation in various industries is a major driver for the growth of the market. In addition, rising demand of AI and IoT integrated systems based on machine vision and initiatives taken by government to support smart factories across the globe are anticipated to boost deep learning in the assessment period. The main application of machine vision system is visual inspection on the surface of components, and detection of small defects in the products. Visual inspection identifies different defects in the components such as scratches, cracks bubbles and measurement of cutting tool wear and welding quality. The deep learning approach for the machine vision helps in designing automated process of machine vision systems. This approach involves image acquisition, pre-processing, feature extraction and classification. Increasing defects in manufacturing process leads to minor quality defects and customer returns to serious product malfunctions, consumer hazards, expensive recalls and even lawsuits. Therefore, demand for quality inspection and automation in various industries is gaining prominence. The quality inspection in manufacturing industry has increased the productivity and revenue of the business. Several technologies such as machine vision and barcode scanner have impacted overall production with better end products to costumer in a hazel freeway. The machine learning algorithms, applications, and platforms are helping manufacturers to find new business models, better product quality, and improve manufacturing operations at the factory level. A recent study by World Economic Forum on future production found that manufacturers are assessing combination of technologies including Internet of things (IoT), Artificial intelligence (AI), and machine learning for improved and accurate asset tracking, supply chain visibility, and inventory optimization. According to an article published recently by Technology and Innovation for the Future of Production the total revenue of artificial intelligence in U.S. region accounted for USD 77.5 billion and that in Europe was of around USD 15.0 billion. Automation saves a lot of time and money required for testing and quality check, moreover once created automated tests can be repeated at no additional cost than manual tests. The machine vision software developers are exploring new methods from artificial intelligence (AI) such as identification of various objects that are in flow of goods within the factory. Deep learning in machine vision involves training and learning of the computers through different architectures such as convolutional neural networks (CNNs). The technologies based on deep learning and CNNs have applications in many different branches of industries for example in electronic industry, healthcare industry and others, The inspection process can be automated and accelerated by using self-learning methods for an easy detection of In food &Beverage industry the machine vision technology is used to detect poor-quality fruits and vegetables before packaging and further processing. Therefore, the need for quality inspection and automation is increasing more effectively across various regions. It reduces inspection cost, improves customer retention, and increases profitability of business. The deep learning software improves the automation and quality check process which increases sales and revenue opportunities for manufacturer

GROWTH IN ADOPTION OF CLOUD-BASED APPLICATIONS

The increased usage of cloud based data for storing and processing a huge amount of data has increased the popularity of the machine vision system in recent years. The manufactures nowadays are considering automation processes for inspection over manual methods. Conventionally, these processes were controlled in-house for cost and security purposes but now situation has changed. The access to the remote cloud computing and storage facilities is augmenting the abilities of any machine vision system in the industrial sector. With the help of cloud technology machine vision system are able to  achieve their full commercial potential as a lot of data is properly stored and managed in cloud computing. The cloud technology hugely increases the speed of front-end analysis and allows manufacturers to experiment with new, more connected solutions.  Cloud computing provides a virtual platform to store data to the manufacturing industry in terms of storage and flexibility. The acceptance of cloud service has increased usage of machine vision system in various industries such as semiconductor, pharmaceutical, automotive and other industries. The machine learning helps in creating algorithms which can use data for predictions based on identified patterns in the data. Business intelligence, IOT, personal assistants, bots and competitive computing allow cloud to collect, store, analyse, and retrieve the data needed for various applications. For example IoT is used for connecting machines to communicate with one another for the exchange of data. The machine learning and cloud services make together make it easy to provide the resources which for the collection, storage, and retrieval of large amounts of data. One of the biggest reasons this combination works because it offers flexibility and scalability.

As cloud service is flexible and it allows provision of the servers with different specifications depending upon the needs of the machine learning are also substantially boosting the growth of machine vision market.

GROWING DEMAND FOR VISION-GUIDED ROBOTIC SYSTEMS

Increasing demand of mechanization, safety and premium quality of products is expected to drive the market. In the last two decades, industrial automation has undergone remarkable expansion. Robot systems have made automation of more challenging, complex tasks easier. The demand for vision guided robots is increasing owing to their more precise, cost effective, reduces the time required and simplifies the inspection process properties.  There are multiple reasons which are contributing in the demand for vision guided robotic system. According to an article published by Cognex about 10% of the robotic system use vision system and a few of them also have utilized 3D vision capabilities, it has been estimated that the number of robotic system using vision may triple in the coming period of time. Increasing adoption of vision-guided robots in consumer electronics and light assembly applications due to their more precise sensors and lower prices are driving the market. Evolving nature of manufacturing industries and great demand for adaptability for the usage of vision guided robotics is fuelling the growth of the market. In order to incorporate robots and create flexible manufacturing the vision system are been deployed on the factory floor. The vision robotic has improved application as compared to the robotic applications such as bin picking, machine tending and welding. Cognex (U.S.) has developed an integrated vision solution for their customers under the brand name Cognex ViDi Suite the product works for 2D & 3D, system and enables different applications such as part location, flexible part feeding, inspection or environmental perception.  Moreover, other key players such as robotic vision technologies is developing vision robotic for various applications such as for tracking, depalletizing, inspection and others. The company has developed eVisionFactory (eVF) which is brand name of the RVT software platform. The product helps the users to create scalable and robust vision guided robotic systems.

Vision systems are anticipated to witness rapid adoption as the standardization and development resources are converging on the next frontier in robotics. The collaborative robots are intended to work safely and effectively in the same space with humans. The exterior sensors for collision detection are simply not enough hence machine vision is the promising option to many collaboration challenges.

Major Players: Global Deep Learning In Machine Vision Market

Some of the major players operating in this market are Cognex, Mvtec Software Gmbh, Qualitas Technologies, Jm Vistec, Cyth Systems and Sualab  among others.

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