Computer vision is a rapidly growing field in the world of artificial intelligence that focuses on enabling computers to process and understand visual data in the same way that humans do. It’s not a new invention, but rather the result of several decades of work in the field.
With the advancements in computer vision in recent years, it has been applied to various industries and has changed the way we approach certain tasks. In this article, we will discuss multiple real-world applications of computer vision.
Self-Driving Cars
Self-driving cars have been a topic of interest for nearly 100 years, but with the rapid advancements in computer vision in the last 10 years, many major automotive manufacturers are now testing autonomous vehicle systems.
Computer vision algorithms are used to track objects around the car and provide inputs for the vehicle to react to its driving environment, which provides safer roads, lower transportation costs, and reduced air pollution and greenhouse gas emissions. Although the exact timeline for the widespread availability of self-driving cars is unclear, it’s only a matter of time before fully autonomous vehicles become a reality.
Waste Management and Recycling
Computer vision technologies, such as AI-based waste recognition systems, are being used in the waste management and recycling industries to identify, check, and analyze waste composition.
The latest systems can sort waste more efficiently and reliably than human workers, from identifying recyclable materials in waste bins to monitoring facilities and trucks, which leads to optimizing the waste management and recycling processes.
Agriculture
Computer vision is used to automate various tasks in agriculture, including plant disease detection, crop monitoring, and soil analysis.
Drones equipped with cameras capture aerial imagery and provide detailed information about the condition of the soil and crops. The data collected from the drones is fed into smart systems that analyze the data and provide reports that enable farmers to adopt more efficient growing methods.
Real-Time Surveillance
With the rise in security concerns, constant surveillance of public places and private organizations has become necessary.
Thanks to computer vision, a single location can be equipped with hundreds of sensors and cameras that are monitored by sophisticated computer vision systems in real-time. The systems can analyze the vast amount of data generated by these devices and send out an alert to the security team as soon as they detect any unusual activity.
Ball Tracking Systems in Sports
Computer vision algorithms have been used for the past 15 years to track the precise trajectories of tennis, cricket, and badminton balls. The systems analyze multiple objects in an image and build a three-dimensional trajectory of the ball’s movement frame by frame, which is crucial for fair refereeing, and computer vision algorithms are capable of building predictions of ball trajectories in real time.
Manufacturing industry
The manufacturing industry has embraced the power of computer vision in its automation technologies to enhance safety, increase productivity, and improve efficiency.
One of the most significant applications of computer vision in this field is defect detection. Gone are the days when trained workers would manually inspect items for flaws in certain batches. Now, computer vision can spot even the tiniest defects, as small as 0.05mm, such as metal cracks, paint defects, and incorrect printing. This is made possible by the use of vision cameras that employ algorithms that act as an “intelligent brain.” These algorithms are trained with images of both defective and defect-free items to ensure they are specifically tailored to each application.
Another important application of computer vision in the manufacturing industry is barcode reading. Optical Character Recognition (OCR) technology, a component of computer vision, can be used to automatically identify, validate, convert, and translate barcodes into legible text. This is useful, as most items have barcodes on their packaging. Labels or boxes that have been photographed can have their text retrieved and cross-referenced with databases using OCR. This process helps in detecting items with incorrect labels, providing expiration date information, publishing product quantity information, and tracking packages throughout the entire product creation process.
Construction Industry
Computer vision (CV) plays a vital role in the construction industry, helping businesses and workers maintain equipment, reduce downtime, and ensure safety. With predictive maintenance, CV can notify staff of potential equipment issues, enabling them to fix them before it’s too late. In addition, CV can also provide PPE detection to ensure that workers are wearing the necessary protective gear.
CV also monitors machinery for potential issues, detecting flaws or changes and alerting human operators. With deep learning, CV can recognize protective equipment in different settings, promoting safety and quick identification and response to accidents.
As we’ve seen, CV is a rapidly growing field that has made a significant impact across various industries. By automating repetitive tasks, increasing crop production, and ensuring safety, CV is truly changing the game. With more and more companies embracing the AI revolution, it’s clear that computer vision will continue to be a major driving force in the transformation of industries everywhere.