Have you ever had a self-visualization of the world. In your head and hoped that one day you could feel its emphasis on being real? Everybody has. Now, that visual vision can be sprung to life, even just for a little while.
How? Through Computer Vision. The goal of computer vision is to emulate the human vision of digital imagery and develop a visual understanding through Artificial Intelligence. By this, it means that through Artificial Intelligence machinery, digital images or objects can be used to accurately identify what can be “seen”.
Whilst there has been a resurgence in this technology. Over recent years with applications such as Amazon Go, Autonomous Vehicles and Face Recognition and Detection. The concept has been around for more than 50 years. It is today that we’re now beginning to realize its benefits.
The goal of computer vision is to extract pixel vision through images and objects and to extract meaning. This can be as simple as detecting material shapes or sharp edges. Some more prudent, real-life examples include:
- Automated Checkouts and Cashiers in Retail – Computer vision has more emphasis within retail probably more than any other industry. Now, customers are using automated checkouts and cashiers using deep fusion sensors to charge customers with their product choice and confirm payment.
- Autonomous Vehicles – Computer vision is necessary for the autonomation of enabling driverless cars. The use of ultrasonic sensors and deep-learning algorithms and use data sets to enable the detection of objects, routes, and traffic through a journey to ensure secure driving capabilities.
- Image Processing – Learning algorithms within machine learning use binary data to characterize geometric elements that build objects within images. Known as low-level processing algorithms, this helps computer vision to perform edge detection, segmentation, and match-making capabilities.
- Manufacturing – Manufacturing is now being driven to be efficient and safe with computer vision. The equipment used is now being monitored using predictive maintenance to ensure the ability to detect any concerns before it breaks down. This is to ensure that action is taken before any costly downtime is administered. Other examples include product quality.
Challenges And The Future Of Computer Vision
Whilst computer vision is incredibly impressive, there are many challenges that still need to be overcome. For example, one of the concerns of humans is accuracy. Is technology such as computer vision accurate enough to be used in the real-world?
It currently cannot be comparable to human activity. Therefore, there is a lot of pressure on algorithms to be developed with accuracy, reliability and be swiftly responsive, especially within difficult conditions and climates.
ML algorithms need to be trained appropriately to execute. Another challenge is through autonomous vehicles. Whilst they are expected to be on the roads by 2021, is there enough scene- understanding for driverless vehicles to enact human response within a journey on the road?
Object classification and detection are currently getting the most attention to ensure computer vision models the environment, and these algorithms are essential to autonomous vehicles.
There is currently a bottomless-pit of concerns and challenges. Although, these are not unknown. computer vision is becoming an exciting reality, Just like fleets geeting smart with mobile phone technology. however, there is some way to go to ensure that human concerns are eliminated.