Computer vision, a subset of Machine Learning, gives machines the power of sight. If put in simpler words, computer vision allows machines to see, owing to machine learning and deep learning algorithms. This technology has garnered acceptance in several industries in a very short period of time, and has become an integral part of technological development and digital revolution. But, there still remains one particular question; “How do businesses profit from the usage of Computer Vision?” Well, machine learning professionals have answers for this because they got their education from some of the finest machine learning institutes. But, those who are not well acquainted with machine learning, may be completely oblivious of computer vision. That is why we have put together a list that contains detailed discussion about the application of computer vision in the sectors of Healthcare and Transportation. Let’s have a look!
Computer Vision in Transportation
The ever-growing demand for the usage of the latest technologies in the Transportation sector has impelled technological development to a great extent, keeping computer vision at the sole centre. From autonomous vehicles to locating parking occupancy, the Intelligence Transportation System or ITS has grown to become a debatable field for promoting the effectiveness, safety, and proficiency of transportation.
Two Most Significant Application of Computer Vision in Transportation According to the Best Machine Learning Institute
• Road Condition Monitoring
Computer vision has been constantly and successfully used for detecting defects on roads. Defect detection is used to make the assessment of whether the road condition is good or not by examining changes in asphalt and concrete. Automated Pavement Distress, also known as PD detection, has given a high success rate in the increase of maintenance allocation efficiency of roads and the decrease of safety risk associated with accidents. Algorithms of computer vision use assembled image data which it later processes in order to build automatic crack detection in roads. It also builds automated classification systems that enable human-independent targeted restoration and precautionary maintenance.
• Autonomous Vehicles
Autonomous cars or self-driving cars are no more a matter of science fiction. They are very real, and there are, in fact, thousands of engineers and developers, worldwide, who are working rigorously to improve the reliability and safety of these self-driving automatic vehicles. Computer vision is used for the detection and classification of objects, such as traffic lights or road signs, to prepare 3D maps or motion estimation. It is a chief role to play in the manufacturing of autonomous vehicles. Self-driving cars work by collecting data from their surroundings, like cameras or sensors, and interpreting it in order to respond accordingly. Researchers who work on Advanced driver-assistance Systems or the ADAS technology, combine certain computer vision techniques, such as feature extraction, pattern recognition, object tracking, and 3D vision, for the development of real-time algorithms that help in driving related activities.
Computer Vision in Healthcare
Healthcare is one sector that requires constant upgrades for providing better treatment to patients, and to build a better infrastructure for hospitals and medical colleges. Medical imaging data is considered one of the richest bases of information. Without the right kind of technology, the healthcare sector may spend more time and energy to get the right results. Without computer vision, doctors would have been compelled to analyse every patient’s data manually, and authorities would have to do all the administrative work manually as well. But, thanks to the rapid growth of technology, healthcare is now one of the fastest sectors to have adopted new, automated solutions.
Two Most Significant Application of Computer Vision in Healthcare According to the Best Machine Learning Institute
• Cancer Detection
Image recognition is a technology that allows doctors to identify any kind of abnormality and change in patients by comparing cancerous and non-cancerous cells in images. Automated detection paves the way for a faster and better cancer diagnosis by using data from Magnetic Resonance Imaging or MRI scans. The healthcare sector is using computer vision extensively to detect skin and breast cancer.
• Digital Pathology
Owing to the abundance of Whole-slide-imaging scanners, computer vision is now capable of processing medical image data for detecting and identifying types of pathology on display. Digital pathology is used for: (1) Thorough examination of sample tissues, (2) Image analysis and interpretation, (3) To assess the accuracy of early detection and diagnosis, (4) Pathology types matching to previous cases. Digital pathology helps doctors save time and make improved decisions.
