Computer Visions have been gaining popularity over time so much so that each one of us has been experiencing and utilising them on a daily basis without even realising it. This article covers all you need to know about Computer Vision and its applications in 2022.
What is Computer Vision?
Computer vision is a subcategory of Artificial Intelligence that helps computer systems visualise, identify, analyse and make conclusions or actions with the help of Machine Learning and Deep Learning models. The most prominent hallmark of Computer vision to date has been automation which has revolutionised the IT sector.
Computer vision has been automating major industries like retail, Production, Healthcare, Sports, Education, Transportation, Fashion, Food, and so on. Not only these, but computer vision has also been benefiting some unconventional sectors like Agriculture/farming and Energy as well.
when it comes to Computer Vision automation, is that the process must be real-time, otherwise, instead of solving the problem, it makes the process even tougher. Computer Vision is not a new concept to the world, but it has taken a proper share of time for use cases to acquire a certain level of accuracy to be used in the real world and not only for projects. Computer Vision can be seen to advance across a range of sectors, especially in the Transportation sector. applications using python and different algorithms combined with advancements in Intelligent Transportation Systems (ITS) which can be seen in:
Based Autonomous Vehicles:
Robo-car production is expected to reach 800,000 units worldwide in 2030. (Statista). For a real-time activity, multiple algorithms like feature extraction, Pattern Recognition, Object tracking, and 3D vision are used in Robo-cars.
There is a lot going on inside an autonomous vehicle, for instance, the generation of live 3D maps using the camera sensors takes place. This way, the vehicle can understand the route, identify obstacles, and adjust its positioning. Another important sensor working inside, an autonomous vehicle is a GPS or positioning sensor which helps it locate the absolute position itself and choose optimised routes toward its destination. Camera and LiDAR sensors to classify different objects and obstacles.
Further limitations like efficient driving during low light, performance with a busy surrounding, and many other areas are being worked upon to make it possible for autonomous vehicles to be able to function on city routes.
Smart Car Parking with Computer Vision Algorithms:
Parking guidance and information (PGI), especially camera-based ones, are already adopted in non-automated cars. Multiple algorithms and techniques carry out a range of different results and precision. An instant can be using algorithms like YOLO and Mask RCNN, along with the combination of RESNET classifiers to differentiate between.
Automated Pavement Distress:
Pavement distress can be easily monitored for the risk of accidents. The classification technique is most commonly used in the systems
Vehicles are a basic means of transport in present-day life. In 2020, Approximately 78M cars were manufactured around the world, Surprisingly,15% of cars were manufactured in the previous year. In this scenario where the usage of vehicles is necessary, road conditions are a great concern. Some of the types of poor road conditions are road cracks, potholes, sloppy roads, broken concrete, etc. A pothole can burst vehicles’ tires and cause accidents.
Traffic Flow monitor using Computer Vision with Python:
Computer vision-based road traffic monitoring systems work with the help of drones and cameras. These monitoring systems not only be used to control traffic and reduce accident risk but can also help design new roads, U-turns, traffic signals, and layout routes by understanding the patterns. Presently algorithms, traffic, vehicle count, and number plate detection can be efficiently done.
Picture from [Gautam Kumar, https://medium.com/@gautamkumarjaiswal/real-time-traffic-monitoring-system-using-python-783288c1c8d0]
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