Market research report published by  MarektsandMarkets "AI in Computer Vision Market analyzes the AI in Computer Vision Market by Component (Hardware, Software), Vertical (Automotive, Sports & Entertainment, Consumer, Robotics & Machine Vision, Healthcare, Security & Surveillance, Agriculture), and Region - Global Forecast to 2023", the AI in computer vision market is expected to be valued at USD 3.62 Billion in 2018 and is expected to reach USD 25.32 Billion by 2023, at a CAGR of 47.54% between 2018 and 2023.The increasing demand for computer vision systems in non-traditional and emerging applications and growing demand for edge computing in mobile devices are among the factors driving the growth of the market.With the increasing labor cost in the security market and use of robotics in the healthcare industry, AI-based computer vision systems are being used for many applications.

Hardware expected to grow at a high rate during the forecast periodThe key factor driving the growth of hardware in the AI based computer vision market is the growing penetration of AI-capable processors in mobile devices, such as smartphones, drones, automotive, and consumer electronics devices.

The major focus is to overcome challenges faced by industrial drones in terms of reliability, safety, and autonomy.

Major players operating in the AI in computer vision market includeNVIDIA (US), Intel (US), Qualcomm (US), Apple (US), Alphabet (US), Microsoft (US), Facebook (US), Wikitude (Austria), Xilinx (California), Basler (Germany), Teledyne Technologies (US), Cognex (US), General Electric (US), and Avigilon (Canada).

The company continues to lead in the development of new products for the AI in computer vision market.

About MarketsandMarkets™MarketsandMarkets™ provides quantified B2B research on 30,000 high growth niche opportunities/threats which will impact 70% to 80% of worldwide companies’ revenues.

How to Use Image Recognition for Your Business?From the business perspective, major applications of image recognition are face recognition, security, and surveillance, visual geolocation, object recognition, gesture recognition, code recognition, industrial automation, image analysis in medical and driver assistance.

Let’s take a look at how image recognition is creating a revolution in some of the business sectors –E-commerce IndustryThe level of adoption of this technology is the highest in e-commerce including search and advertising.

It presents a more interactive view of the world by making everything they see searchable.A prominent example of image recognition is CamFind API by Image Searcher Inc. Its technology enable an advanced level of mobile commerce.

CamFind identifies objects like watches, shoes, bags and sunglasses etc and returns purchasing options to the user.

Prospective buyers can perform live product comparison without visiting any website.

Cars of the future are expected to detect obstacles and warn you about proximity to guardrails and walkways.

Especially amid movement restrictions induced by the COVID-19 pandemic, research shows that global online sales jumped to $26.7 trillion in 2020.

With the rise of ecommerce, one thing is abundantly clear: brick-and-mortar retailers need to innovate if they want to stay competitive.

Among the most promising applications of computer vision include inventory management, loss prevention, automated checkout, and behavioral analytics.

The company holds 23 patents on its technology and can analyze images from phones, in-store cameras, and grocery store robots.Trax uses computer vision technology to scan shelves in stores and identify what is neededAutomated checkoutStandard.ai: Previously known as Standard Cognition, Standard.ai’s automated checkout solution is made to fit with retailers’ existing stores and technology.

Standard doesn't use any facial recognition or biometrics, and all deployments are on-premise to ensure maximum performance and security for retailers and shoppers alike.Trigo: Using proprietary algorithms and affordable off-the-shelf sensor kits, Tel Aviv-based Trigo allows retailers to analyze anonymized shoppers’ movements and product choices in real time.

Once a customer reaches a certain threshold, the system sends an alert, along relevant video clips, to the appropriate staff member.Clips from the VaakEye product demo videoBehavioral analyticsDeep North: Deep North provides an analytics platform that builds real-time video intelligence for retailers based on video data from CCTV and other cameras that those retailers already use.

When these modes of transport carry so much material, it becomes crucial to monitor and track their movement across the globe.Satellite Image analysis provides a great solution to such problems.

These image analysis not only help in tracking the given subject but also helps in monitoring the surroundings of the target.

With ships detection, and combined with data of other ships routes and live status, (Data from other sources), many security features can be built.

Also, search and rescue operations can be further improved in terms of their response time and location accuracy.

These Applications require large quantities of data, and with the recent availability of satellite images from many tech giants and government organizations, it has become an emerging field in the aerospace domain.Rhammell on Kaggle provided a cleaned dataset of satellite imagery, separately for each of the categories, i.e.

Images were derived from PlanetScope full-frame visual scene products, which were ortho-rectified to 3-meter pixel size.

It is used to monitor climate conditions, for aerial photography, video inspection, aerial mapping, drone delivery, and more.In recent times it is used for search & rescue operations post any natural disasters.

It is best to locate a missing or kidnapped person in a vast area.How is search and rescue operations carried out using drone image?Individual rescue using Drone ImageryThe real-time imagery provides crucial information about emergencies.

It provides an accurate location over a vast area so that rescuers can take quick decisions and act on it immediately.

They can send the team to specific areas where help is needed.

Drones can use floodlights to carry out nighttime rescue operations.

The footage of search and rescue is used for future training purposes as well.Hazardous Chemical SpillsIn case of hazardous chemical spills or leakage of nuclear chemicals from factories, damage assessment can be a risky task.

According to a survey conducted by the United States in 2014, within 64 years, the urban population increased to 3.9 billion from just 746 million.

The growth of urbanization can vary between countries, states, etc.

On a global level, too, survey conducting organizations use them to make an overall calculation of expansion in urbanization.

Machine learning is one such concept that can widely be used in the process of urbanization.

There are a variety of methods that can be used to present a clear picture of the urbanization which can take place in a particular location.

The data that are produced using these methods are far more accurate and are completely based on the images of satellites.

It is important as it helps to identify existing or/and potential hazards.

Many of these lines are inaccessible due to a lack of transportation and geographical locations.

The undetected faulty gas lines and power lines had led to a forest fire.

They also face certain limitations such as inaccessible terrain, contact with high-voltage power lines, hazardous chemicals emission, and more.Using Drones for Utility InspectionsDue to the above, many companies have started to use drones as the inspectors can survey the structures or lines from a safe distance and can cover difficult terrains & conditions with ease.

Few examples of utility inspections carried out using drones are:Power transmission line– to identify foliage encroachment, sagged wires, fuels buildup that leads to forest firesVertical constructions– to check signs of irregularities and damageBridges & overpass– to check signs of damage or cracksWater systems– to identify leakages, management of vegetationDams– to check structural defects and identify the repairs neededDrones improve the quality of such inspection as it allows the inspectors to conduct frequent inspections and to collect more data.

Thermal imagery, hyperspectral, color, light detection & ranging are the sensors used.

Since the inception, the most widely used techniques for inspection are the manual inspection which includes either foot patrol or/and conventional helicopter-based inspection.

The detailed approaches used for segmentation, object detection, and anomaly detection are described in the following section.Powerlines SegmentationThe Powerlines Segmentation is used to separate the powerlines (the power transmission cables) from the entire image so that an inspection algorithm can just focus and analyze only the power lines while staying unaffected by the surrounding or the background of the powerlines.

It consists of convolutional layers on skip pathways which bridges the semantic gap between encoder and decoder feature maps, thus aiding in improving the gradient flow.DatasetsDataset 1:We have obtained 200 Visible Light (VL) spectrum images from the https://data.mendeley.com/datasets/twxp8xccsw/1.

It consists of 200 images of size 512×512, along with the binary wired image masks for all the input images.Dataset 2:The other set of images was obtained from  https://data.mendeley.com/datasets/n6wrv4ry6v/8.

Further, data augmentations were carried out on Dataset 2 same as Dataset 1.Training and Segmentation Methodology Nested U-Net architecture, as the name implies, makes use of nested and dense skip connections between encoder and decoder apart from the typical skip connection used in U-Net Network.

Like the IoU, they both range from 0 to 1, with 1 signifying the greatest similarity between predicted and truth.

OpportunityChemical supply chains forced to adapt to new expectationsAs global commerce continues to make its migration from “the main street” to “mainframe”, organizations across all industries have been investing vigorously in ways to digitize their operations.

eCommerce has become a norm in everyday life for most consumers, and as expectations for the same frictionless experience continue to make their way towards other industries, the chemicals industry has been no exception.The next generation of purchasing managers increasingly values speed, transparency, and ease of doing business above other factors.

Despite this fact, eCommerce adoption in the industrial B2B setting has been evolving at a much slower pace when compared to industries that sell directly to consumers.

Industrial manufacturing companies are challenged with the changes needed to existing habits, processes, and systems, to adapt to the evolving expectations.

A few years ago, one company that had been manufacturing and distributing high purity chemical products for over 25 yearsrecognized that the traditional way of doing business was no longer able to keep up with the evolving needs of customers and suppliers.Too many links in the chemical supply chain, combined with manual processes along the way, not only prevents companies from delivering a better customer experience, but also limits the visibility and insights that manufacturers need to optimize inventory and production.What was needed was a truly digital point of sale and supply chain experience for all stakeholders involved, capable of providing real-time pricing, inventory, and demand signals.To stay in front of the competition, the company asked Aventior to help design, develop, and launch the first truly digital marketplace for the chemical industry.SolutionCombining scalability with industry-specific needsOver the course of 10 months, Aventior had completed the design, development, and launch of the industry’s first truly digital eCommerce marketplace for the chemical industry.

With scalability and user experience at the forefront, the platform also needed to incorporate specific features and functionality to support the chemicals industry.Third-party connectivity was another critical component of the platform – it had to be capable of sending and receiving information seamlessly with external systems to deliver an end-to-end digital experience, whether it be with logistics providers, suppliers, buyers, and other third-party tools.

Using data curation tools, engineers can get a better understanding of the data they’ve collected, identify the most important subsets and edge cases, and curate custom training datasets to feed back into their models.The role data curation tools play in machine learningThe best data curation tools enable you to:Visualize large scale data: Make it easy to obtain insights on key metrics, as well as the general distribution and diversity of your datasets regardless of sensor type and format.

Curate diverse scenarios: Identify the most interesting segments within your dataset, and manipulate them within the tool to create completely customized training sets.Seamlessly integrate: The tool should fit well within your existing workflows and toolset.What are the best data curation tools for computer vision?With an overwhelming amount of AI products and platforms popping up year after year, how do you know which will provide the most value?

Based on our experience, we are sharing our honest reviews of the top tools, hoping that this will be of use for engineers searching for a data curation solution.Read on below to find out which data curation tool is the best fit for your computer vision project.Aquarium LearningAquarium is a data management platform that aims to make it easy to identify labeling errors and model failures.

With Aquarium, users can version and combine model predictions with their ground truth.Aquarium is especially focused on curating and maintaining training datasets, catering less to raw data management use cases.

They also support multiple annotation types, such as classification, detection, and segmentation.Interactive model evaluation - Users can manipulate evaluation thresholds and obtain interactive visualizations to obtain required samples quickly.Collaborative features - Users can collaborate with each other on the Aquarium platform to build data subsets, associate them with issues, and identify new data for annotation.FiftyOneDeveloped by Voxel51, FiftyOne is an open-source tool to visualize and interpret computer vision datasets.

Today, the platform lacks collaborative features; for example, a single instance cannot host multiple user accounts.Key Features:Model & dataset zoo - FiftyOne taps into TF and Pytorch dataset zoos to provide access to a variety of open datasets and open-source models.Advanced data analysis - Via the Brain, a separate closed-source Python package, users can quantitatively assess the uniqueness, mistakenness, and hardness of data.External integrations - FiftyOne directly integrates with popular annotation tools such as LabelBox.

Open data is fueling commercial and technological advancement in autonomous driving—one of most well known resources being the nuScenes dataset.Developed by the team at Motional (formerly nuTonomy), nuScenes is one of the most popular open-source datasets for autonomous driving.

The nuScenes dataset enables researchers to study a wide range of urban driving situations using data captured by the full sensor suite of a self-driving car.

Recorded in Boston and Singapore, nuScenes features a diverse range of traffic situations, driving maneuvers, and unexpected behaviors.The dataset includes:Full sensor suite: 32-beam LiDAR, 6 cameras and radars with complete 360° coverage1000 urban street scenes, 20 seconds each1,440,000 camera images23 classes and 8 attributesAccessing nuScenes data in SiaSearchTo access the data yourself, you’ll need to sign up for a free account on SiaSearch.

This view lets you quickly understand the overall dataset composition, as well as identify any gaps in data distribution.Querying the nuScenes DatasetHaving a holistic view of the dataset, while useful, is not enough.

The ability to drill into specific subsets can uncover insights and imbalances in the data—a critical step in model building and validation.SiaSearch makes every piece of nuScenes data searchable against all available and auto-extracted dimensions using its intelligent search interface.

The platform features two ways to search for the exact sequences you want, using either a visual or code interface.

Aspectum has signed a contract of cooperation with Aventior to improve its Change Detection and Object Detection capabilities.Aspectum will utilize Aventior’s processing algorithms to develop its data analytics and improve the in-house Aspectum machine learning model.

With the help of these technologies, Aspectum will be able to conduct object detection operations such as data collection, processing, and visualization.Aventior’s technology is powered by a Neural Network capable of classifying the differences between two images by comparing pixel combinations.

It builds a map, where each pixel represents a “neuron,” and models the spatial context of these pixels in order to spot the correlations between them.

Simply put, the network distinguishes trees from buildings, and buildings from roads.Specifically, Aspectum will be using algorithms for Change Detection, Car Detection, Building Detection, and Vessel Detection.

This means Aspectum will be able to help businesses and non-profit organizations alike to take advantage of spatial intelligence and satellite imagery on a fundamentally different scale.Aspectum clients will have access to a tool able to automatically collect and analyze object data and hence, track changes for urban, suburban, rural, and maritime areas.

The maximum territory coverage has also been extended exponentially compared to previous software versions, while setting validation accuracy near 95%.This new machine learning algorithm is best suited for the following sectors:Infrastructural planning and renovation projects (building detection, traffic hubs workload estimation, violations tracking)Oil & Gas production industry (estimate oil production based on a number of oil storages, pipelines, supply chains analysis, and working oil wells)Transportation management (route planning, load estimation)Marketing research (parking lot load, location intelligence)About AventiorAventior specializes in data science, computer vision, data analytics, and cloud engineering business-to-business solutions.About AspectumAspectum is a California-based company focused on providing high-quality visualization and powerful analytics for business outcomes.

Resolution Reconstruction of Urban Images (Satellite Images) using AIIt is said that “A picture is worth a thousand words”.

Computers can understand these images in terms of color values at a pixel level, and the features of the contents.

The CV has now become a popular topic of academic and industrial research applications.

The quality of the image is dependent on the sensors which capture these images, and sensors can be very expensive if extremely high image quality is expected.With Artificial Intelligence (AI) it is now possible to improve the image quality up to a certain extent, without much loss of data.

The quality of images produced by sensors is limited, mostly due to sensor hardware limitations which cannot be much experimented with, as the sensor hardware is a costly piece in the system.

One such important type of enhancement is resolution improvement, especially in the field of Satellite Imagery.In satellite images, the image resolution denotes the distance on the ground that is captured by each pixel.