In the age of rapid technological advancement, Computer Vision emerges as an important force, effecting many industries. From healthcare to automotive, its applications are changing the way we perceive and interact with the world. This article explores Computer Vision’s impact, uncovering its critical role in enhancing diagnostics, safety measures, and beyond.
What is Computer Vision?
Computer Vision is a technological branch of computer science that focuses on expanding a computer’s understanding of digital media. It is usually integrated with multiple Machine Learning models and Pattern Recognition algorithms to perform various tasks and operations. Systems can be trained to identify objects and classify them according to similar attributes accurately, when integrated with Deep Learning models.
These systems are especially useful in industries like automotive and healthcare, especially due to their malleable abilities – like detection and recognition systems. Many companies today are adopting these systems to not only automate tasks but also reduce the chances of human error by using this understanding to perform tasks more efficiently.
Applications and Examples of Computer Vision
Healthcare Industry
An industry that has greatly benefited from the adoption of Computer Vision is the healthcare industry. It is an efficient tool used for automating tasks and can be used for tasks like diagnosis and Imaging. This is a result of Convolutional Neural Networks (CNN), systems that are highly efficient in obtaining integral information from data and can hence be used for image detection and many more uses. In the healthcare industry, this advanced technology contributes to tasks like:
Medical Imaging
Computer vision is used to identify any anomalies in X-rays, MRIs, and CT scans when integrated with CNNs and Pattern Recognition algorithms. This serves an integral role in diagnosing diseases with high accuracy levels.
Disease Progression Detection
This application came into play after the catastrophic effects of COVID-19. Due to its quick progression all across the world, computer vision was used to make distinctions between critical patients – allowing for an efficient screening process. The systems were embedded with Deep Learning models that were able to track the respiration pattern of the patients and hence diagnose their condition accordingly.
An example of this application is Google Health’s AI system Mammography Screening. They have developed an Artificial Intelligence system, that leverages the ability of computer vision to analyze mammography images for breast cancer screening. Intending to increase accuracy, Google Health’s system has reduced false positives by 5.7% in the U.S. and 1.2% in the U.K. and reduced false negatives by 9.4% in the U.S. and 2.7% in the U.K.
Automotive Industry
Computer Vision plays an integral role in the automotive industry, influencing sectors like vehicle manufacture, inspections, and safety standards. The large sums of data train these systems depending on the operation in question. Some of these operations include:
Autonomous Driving
Artificial Intelligence plays an integral role in the development of the technology that goes behind the self-driving vehicles. Computer Vision is a branch of this system and is commonly installed within Advanced Driver-Assistance Systems (ADAS). The models help in Obstacle Detection and Avoidance, Lane Detection and Lane Keeping and Collision Avoidance.
Vehicle & Driver Safety Tools
Computer Vision powers embedded systems like Automated Parking and 360-Degree Cameras in the automobile. These systems are trained by a multitude of data and uses Deep Learning to identifies objects in the vehicle’s blind spots and allows the driver to respond immediately.
Further, many vehicles also come with Driver Monitoring Systems that are powered by Computer Vision as well. The cameras installed within the vehicle’s interior is used to monitor a driver’s face and eye movements to detect signs of fatigue or distraction. This allows for faster response time and provides a layer of protection to both, the driver and the vehicle.
Vehicle Inspections
Many companies today have developed systems that utilize Computer Vision and Machine Learning Models to perform vehicle inspections. These systems are commonly used during car handovers. A vehicle goes through multiple handovers during its tenure, which increases the chances of it sustaining minor to major damages.
To avoid these damages, many companies conduct vehicle inspections before and after they receive the vehicle. Computer Vision stands at the forefront of this technology and is used to identify any anomalies on the vehicle. It has become popular due to its ability to perform the task efficiently and within less time.
Focalx is an example of a company that has created this technology. Their system consists of complex machine learning algorithms and highly trained computer vision models that determine a vehicle’s condition based on the images provided.
These technological tools are not only efficient in identifying the damages, but are also capable of categorizing them according to the levels of severity. This not only allows companies to mitigate costs accordingly but also identify the causes of the damage.
General Use Cases
In a more general sense, Computer Vision can also be used for developing systems that are used for mundane tasks. These applications include:
Facial Recognition
Computer Vision is the main technological tool behind the development of Facial Recognition systems. It allows systems to identify individuals based on their facial features. This personalized system is powered by deep learning Convolutional Neural Network (CNN) that identifies the specific aspects of a face and stores unique features individually. This application is used in security systems, law enforcement, and unlocking smartphones or other devices.
Optical Character Recognition
Optical Charater Recognition or OCR is a technology that can be integrated with Computer Visions systems, and can be used to convert text from scanned images to electronic data that can be read easily digitally. This method is common for digitizing texts so they can be manipulated for further use. OCR is used by multiple companies that are looking at digitizing data, and is commonly used by the general public as well. There are multiple applications online that allow for the extraction of data from scanned images of documents or papers.
Augmented Reality (AR) and Virtual Reality (VR)
Mainly common in entertainment and gaming, Computer Vision is widely used for AR & VR experiences. These hyper-realistic realities are created through the integration of Computer Vision in the systems. Computer Vision aids in detecting real-world objects (through object detection) and hence, allows the system to impose virtual features upon them. This not only creates a realistic experience, but also allows the audience to interact with the objects and increases their level of satisfaction with the hyper-realistic experience.
Conclusion
In conclusion, computer vision is a technological advancement that has embedded itself in multiple industries with applications that span across a diverse range. From enhancing healthcare diagnostics and improving the automotive industry operations, to providing new user experiences in entertainment and increasing security measures, this technology is a great enabler for innovation and efficiency. It has the ability to accurately analyze visual data through machine learning and deep learning models and can hence lead to important changes in things like automation, safety and healthcare.