Vision analytics has always been considered a game-changer in the industry. It was expected to revolutionize the way security tasks were performed. Improving operational efficiency was another aim of vision analytics.
Both the public and private entities are leaning towards computer vision analytics to revamp their business processes and gain the top position in the markets. Artificial intelligence, machine learning, deep learning, 3D imaging, etc., are some terms we often hear when people talk about vision analytics.
We often read about vision analytics retransforming modern enterprises and SMEs.
Before we see more about what these mean, let’s understand what computer vision analytics is.
The process of analyzing digital image/video signals to understand the visual world using the latest technologies in place of the human eye is known as vision analytics. Identifying intruders & impostors, recognising & tracking objects, identifying behavioral patterns etc.. are some examples of vision analytics.
The global computer vision market anticipates having a CAGR (compound annual growth rate) of 7.6% from 2020 to 2027. There has been a significant escalation in the demand for computer vision services during the last year due to the COVID-19 pandemic.
Taking the increasing adoption of vision analytics into account, we can say that the following trends are going to rule the industry in the coming days.
AI has made it possible to analyze vast amounts of data in less time. Data can be in any form- text, images, or videos. Artificial intelligence in vision analytics is used to examine videos and detect patterns. It helps to identify and predict events based on existing data. The systems can communicate with each other and alert the user about a potential change in the pattern.
For example, AI in the security department is used to analyze videos and identify suspicious activity such as trespassing, sneaking, breaking in, etc. Vision analytics can help detect the change before the actual event takes place and alert the concerned authorities. In the retail sector, AI in vision analytics is used to identify customer behavior patterns and purchasing trends.
Even though machine vision and deep learning are two independent elements, they complement each other and have abilities that overlap. Deep learning has given machine vision a new dimension. Neural networks are an example of deep learning that works well with machine vision. It helps identify the presence in an image/ video frame. It determines if the presence is good news or bad news. We can call them image-classifiers.
Deep learning also helps in increasing the speed of a business process by improving operational efficiency. Many machine vision consulting services include artificial neural networks (ANNs) to provide a comprehensive system for automation in the manufacturing industry.
Thermal imaging is the process that uses infrared and heat radiation to detect objects in the dark. The thermal cameras can distinguish the difference in temperatures so that we can detect the warmer objects/ beings. It becomes easy to identify the presence of a person or an animal against the cold and dark background.
When terminal imaging is used with vision analytics, it sends alerts only for a fixed range of temperature levels. For example, the movements of trees, winds, vehicles, etc., are usually false positives when you want to find a human presence. This is especially useful for security purposes. The percentage of false security alerts can be reduced, thereby improving the efficiency of the security system.
Do you know that the 3D vision market is estimated to have a CAGR of 9.4% from 2020 to 2025? It is the next big thing in the market as the demand for quality inspection of the end products is touching the skies. With SMEs and large-scale enterprises wanting to automate their business, they are turning to 3D vision analytics for high-speed imaging, vision-guided robotic systems, and surface profiling.
3D imaging and vision analytics are also important as the industry is shifting from standard products to personalized products based on customer requirements. 3D smart cameras are said to rule the industries in the coming years. 3D imaging also helps in logistics for autonomous navigation via object detection, self-localization, etc.
Liquid lenses are single optical elements but with an optical liquid material that is capable of changing its shape as and when required. They are used in smart cameras and smart sensors though now we can find them being used in various fields such as biometric recognition and data capturing, reading barcodes, digital photography, and more.
Heavy industries are investing more in liquid lenses to help with various manufacturing applications. The lenses have great focus and adjust to the changes in the voltage and current automatically. Apart from industries, public spaces are also going to be monitored using liquid lenses to track if people are following the safety norms or not.
In simple terms, embedded vision is the integration of a camera and a processing board. Instead of having more than one device to stay connected and deliver us the results, embedded vision systems directly work with algorithms.
When an embedded system (a microprocessor-based unit) is combined with computer vision technology to digitally process the images/ videos and use machine learning algorithms to share the information with other cameras and systems in the network, it is known as embedded vision.
The main reasons for embedded vision systems to become popular are low cost, lesser energy consumption, smaller in size, and lightweight. Embedded computer vision consulting services are used for robotics in the manufacturing industry (for factory automation), the healthcare sector (for medical diagnosis), gesture recognition (for transportation and logistics), the famous facial recognition systems and many more.
Several multinational organizations and public sector industries have adopted vision analytics to retransform their operational processes.
Below are some ways to see vision analytics retransforming modern industries in the global market.
If workplace safety wasn’t as important earlier, it sure is now, thanks to the pandemic that is still shadowing us. The responsibility of ensuring a secure workplace for the employees lies with the top management. This led to many organizations investing in vision analytics. What was supposed to take around 5 years happened in just 6 months.
Risk management in terms of safety standards is being analyzed through vision analytics. Automated monitoring through the cameras and linking them with the machine learning algorithms help companies implement the necessary social distancing norms and promote safe working practices.
A similar approach is being followed by the government agencies to ensure safety in public places. It is said that the need for vision analytics will further increase during the next few years.
The manufacturing industries are adopting computer vision technology to streamline and automate the manufacturing and quality testing processes. From the food industry to the automotive and steel industries, vision analytics is automating and optimizing visual inspections. This increases the flexibility and accuracy of the processes.
Computer-based visual inspections are faster, accurate, and cost-effective. In a scenario where faster production cycles are essential for an enterprise to survive in the competitive market, this technology comes as a boon. Detecting defective products through an automated process saves time and money, allowing manufacturers to release high-quality products into the market in less time.
Edge computing is the process of analyzing information directly where the data is collected. The data is not moved to servers or the cloud for storage and then processed. The advantage of using edge computing is that it saves businesses from moving huge amounts of data to the cloud. Only the relevant information is stored for future use. Also, enterprises can respond in real-time and find solutions faster than before.
This will reduce complications created by network accessibility and latency issues. By combining edge computing with vision analytics, a computer vision consulting firm can help businesses save hours of labor-intensive work required to manually monitor, process, optimize, and analyze loads of data (images and video recordings). The aim is to create frameworks that can seamlessly integrate asset monitoring data with video insights.
It is a fully automated system in which the control actions are dependent on the output action in one way or another. Facial recognition is a great example of using vision analytics in closed-loop systems. Another example would be autonomous cars and driverless vehicles (which are yet to become a part of our lives).
The same theory is being applied to develop systems for industrial usage. The focus here is to create closed-loop systems that optimize the business processes and control the parameters without any intervention from the employees. By adjusting the parameters, the output can be consistent and of the same quality.
For a computer vision consultant to work on the video analytics software, the first thing he/ she needs is hardware that supports the latest technology. We know how much money the firms have to spend on getting rid of the outdated hardware and investing in building a new IT infrastructure every time. The latest vision analytics software is being developed in such a way that it would work with the existing infrastructure. The software can be customized and scaled, while we cannot do the same to hardware. The software is going to change even the traditional cameras to AI-based models to collect and analyze data in real-time.
Computer vision models have been used for various purposes, from detecting objects to tracking events and automating processes in different industries. While the accuracy rate of these models is quite high, we also need to know how vision analytics works.
For example, if it detects an animal, we need to know which reasoning the algorithm has used to identify the animal and give it a name. There are already a few AI-based models that explain how the images or videos have been classified and how the prediction was made without human intervention. We can safely say that more such tools will be developed to help us understand the working of computer vision models. This will further allow us to improve the models and increase their accuracy rate to 100%.
Data annotation is the method of adding tags to the existing data. By adding relevant tags to the text, images, and videos in the dataset, you can make the data relevant and useful across the enterprise. This is a labor-intensive work where data annotators spend hours and days on labeling and adding tags. This helps in future-proofing the workflow.
Using artificial intelligence has led manufacturers to streamline the workflow to improve the quality of production. And now, by combining vision analytics with this setup, it becomes possible to speed up the production pipeline and minimize errors to ensure a faster output.
Data triangulation is the process of using data from different sources or following various ways to analyze data and get real-time insights. Vision analytics is being used for data triangulation by the military and defense departments. Sensor data, videos, and images from drones are collected and combined to create a multi-level security system.
Features such as vehicle detection, user entry access control, detecting intruders, detecting unidentified objects, etc., can be automated. The authorities will have better control over the region/ area and can use the insights to tighten the security systems and prevent lapses.
Conclusion
With the right computer vision expertise and experience, you can retransform your enterprise by integrating near staple solutions with the existing setup. Vision analytics can be adopted by businesses, firms, organizations, and manufacturing units across all industries. It is a comprehensive, reliable, and accurate way to streamline operations and optimize the use of resources.
Vision analytics is changing the way people look at data and insights, and it is necessary to become a part of this change. The future of modern industries relies heavily on vision analytics.
They have the experience and agility to understand what’s possible and deliver to our expectations.
