The key factor, 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 more tedious. Computer Vision is not a new concept to the world, but it has taken a fair share of time for Computer Vision use cases to acquire a certain level of accuracy to be used in the real world and not just as a Computer vision project. Computer Vision can be seen advancing across a range of sectors, especially in the Transportation sector. Computer Vision applications using python and different algorithms combined with classification algorithms have carried out rapid advancements in Intelligent Transportation Systems (ITS).
In this article, we have strung together some of the most important use cases of computer vision that have catapulted technological advancement in the transportation sector:
There are of course a plethora of other use cases that Computer Vision offers. Check out this short video below to learn more!
Now let’s get started!
Computer Vision-Based Autonomous Vehicles
An autonomous vehicle senses its surrounding conditions and acts with zero human interaction. There are different levels of autonomous vehicles ranging from fully manual to fully autonomous. Annual production levels of robo-cars are expected to reach 800,000 units worldwide by 2030. (Statista). For a real-time activity, multiple Computer Vision algorithms like feature extraction, pattern recognition, object tracking, and 3D vision are used in combination.
How does a Computer Vision based autonomous vehicle work?
There is a lot going on inside an autonomous vehicle, for instance, 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, and autonomous vehicle is a GPS or positioning sensor which helps it locate the absolute position of itself and choose optimized routes towards destination. Camera and LiDAR sensors to classify different objects and obstacles.
There are different types of sensors installed in different parts of an autonomous vehicle. The wheels contain curb detecting sensors. Radar sensors monitor the positioning of different nearby vehicles and measure the distance between surrounding vehicles to avoid collision and damage. All of the sensory information is then processed by intricate software technology coded with high-level robust instructions that control the car’s speed, acceleration, braking, steering, and other functions. All this software technology is backed up by advanced AI models.
Limitations of Computer Vision-based autonomous vehicles
The development speed of autonomous vehicles using AI and Computer Vision makes us wonder why we still have not been able to see any fully autonomous vehicles? There are multiple reasons for that:
Weather
The weather is not the same all around the globe. It is still unclear how fully autonomous vehicles will perceive roads and routes when there is snow covering lane marks, or during precipitation.
Radar signals
Since a huge dependency of computer vision-supported autonomous vehicles is on the lidar/radar sensors to drive and realize the presence of other vehicles and obstacles, it is highly possible that with multiple autonomous vehicles in the vicinity their lidar signals might get disrupted. It is also unclear whether it will be possible to mass-produce these autonomous vehicles or not when all of them will need a specific frequency range.
Accountability of accidents
The most frustrating scenario is that It is unclear who to hold accountable for the accidents that occurred by autonomous vehicles. Fully autonomous vehicles don’t have a physical steering wheel or dashboard for the car owner/passenger to take over the car during any crisis.
Further limitations like licensing, governmental laws, efficient driving during low light, performance with a busy surrounding, noise, and many other areas are being worked upon to make it possible for autonomous vehicles to be able to function on city routes in real-time.
Smart Car Parking with Computer Vision Algorithms
A smart car parking system guides the driver in real-time about the availability of free parking spaces to save time and maps out the whole parking area intelligently so as to adjust a maximum number of car parking. Computer vision has a strong role in the smart car parking systems in combination with IoT and AI techniques. Google maps and other apps have this system of analyzing the space and finding out empty spaces for parking.
Computer Vision Algorithms supporting Smart Car Parking
Parking Guidance and Information (PGI), especially camera-based one, is already adopted in non-automated cars as well. Multiple Computer Vision algorithms and techniques carry out a range of different results and precision. An instance can be using algorithms like YOLO and MaskRCNN, along with the combination of RESNET classifiers to differentiate between different car models and other obstacles.
Car parking detection using cameras has acquired high performance even in certain conditions with constraints of low light and bad weather. This can be combined with other detection techniques like classification for obstacle detection or number plate detection for better results.
Automated Pavement Distress using Computer Vision
Automated Pavement distress system realizes bad road conditions and helps the in-charge authorities / municipal officers locate and take action accordingly. With computer vision, pavement distress can be successfully monitored reducing the risk of accidents. Classification technique is most commonly used in the systems for this purpose.
Vehicles are a basic means of transport in present day life. In 2020 alone, approximately 78M cars were manufactured around the world, surprisingly, it is 15% less than the number of cars manufactured in 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 condition are road cracks, potholes, sloppy roads, and broken concrete etc. A pothole can burst vehicles tires and cause accidents.
To automatically detect the poor road conditions, the computer vision based techniques that are majorly used include some of the conventional techniques spectral segmentation and edge detection along with Convolutional Neural Networks. All of these techniques have been worked upon to perform in real-time.
Traffic Flow monitor using Computer Vision with Python
One of the most used use case of Computer vision is the traffic flow monitoring system. This technique is widely used to monitor over-speeding and other traffic rules violations. Surveillance cameras are installed which monitor each vehicle passing by. Traffic flow monitors the speed of vehicles along with number plate info.
Computer vision based road traffic monitoring systems work with the help of drones and cameras. These monitoring systems can not only be used to control traffic and minimize accidents ratio but can also help design new roads, U-turns, traffic signals and lay out routes by understanding the patterns. Traffic density, vehicle count, and number plate detection can be efficiently done with present Computer Vision algorithms.
Some of the derived automated use cases of Traffic Flow monitoring system are Smart Highway Toll Collections System and Smart Gate Opener. All of these types of systems collect data from vehicles using Computer Vision techniques and perform action accordingly.
Picture from [Gautam Kumar, https://medium.com/@gautamkumarjaiswal/real-time-traffic-monitoring-system-using-python-783288c1c8d0]
Frequently Asked Questions
What does computer vision include?
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs.
Source: https://www.ibm.com/topics/computer-vision
Which are common use cases for computer vision?
Retail
Agriculture
Healthcare
Transportation
Manufacturing
Read more at https://viso.ai/applications/computer-vision-applications/
What are examples of computer vision?
Drone monitoring
Autonomous Vehicles
Human Pose Estimation
Smart Farming
Medical Imaging
Smart Glasses
Facial Recognition
Attendance and Engagement Monitoring
Defect Detection
Interactive Media
Traffic Flow Analysis and many more…
What are the new advancements in computer vision?
Deep Learning
Edge Computing
Semantic Instance Segmentation
Augmented and Virtual Reality enhanced Merged Reality
Is computer vision in demand?
There is a growing demand for AI-enabled computer vision systems mainly in consumer electronics like smartphones, laptops, and desktops. The healthcare industry is expected to experience the highest growth rate though.
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