Deep Learning in Computer Vision: A Game Changer for Industries
By Umang Dayal
March 12, 2024
Humans learn from experience and so do the machines.
Deep learning is an application of AI that can improve its performance with more data, after which it can perform specific functions such as driving a car, detecting fraudulent activities, recognizing voice commands, and much more. In this blog, we will discuss the application of deep learning in computer vision and how it's transforming various industries.
What is Computer Vision?
Computer vision is an application of artificial intelligence that allows machines to interpret and extract information from visual data such as images, videos, and texts. The goal of CV systems is to develop smart computers that can recognize and process visual content to perform dedicated functions. This technology has been prominently used in autonomous cars, video surveillance, supply chain management, agriculture, etc.
Understanding Deep Learning
Deep learning is a machine learning process inspired by the human brain functionality. It uses artificial neural networks (ANN) to train and develop large datasets using multiple layers of information units called Neurons. Each neuron is trained to perform its operation and sends its output to other neurons until the desired result is achieved. These neural networks are developed on multiple parameters enabling them to process complex information.
The most widely used neural networks are:
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Generative Adversarial Networks (GAN)
The most successful neural network in computer vision is CNN which has been successfully implemented in the healthcare and aerospace industries.
How Deep Learning is Transforming Industries?
Transportation
Deep learning has allowed seamless analysis of traffic using relatively cheaper surveillance systems. Using a wide variety of sensors such as LiDAR, CCTV, and thermal imaging, it is much easier to track, identify, and segment vehicles in populated cities. Computer vision enables automatic detection of road violations such as speeding, wrong-way driving, illegal turning, skipping red lights, and accidents.
Deep learning systems have facilitated the widespread implementation of effective parking occupancy detection using CCTV cameras. This benefits parking spaces with low-cost maintenance, easier management, sophisticated installation, and better space allocation.
Healthcare
Using deep learning models such as image recognition scientists and medical experts can differentiate between cancerous and non-cancerous MRI scans. In practical use cases, deep learning has been highly effective in detecting serious health conditions such as strokes, heart attacks, skin cancers, etc.
Computer vision allows clinical diagnostics to accurately analyze patient movements using pose estimation algorithms. The rehabilitation programs built using deep learning models are helping patients to move correctly and prevent any future injuries.
Sports
Deep learning models can identify the patterns and movements of athletes through images or recorded videos. Cameras installed above and below the water level can accurately detect swimmers' poses in real time. Using convolutional neural networks trainers can automatically gather necessary data to improve the speed and body movements of athletes.
Computer vision is also used in Tennis to detect and categorize player strokes, which can be later analyzed by instructors to improve player strength and agility. In team sports, deep learning methods such as motion analysis are utilized to gather trajectory information from recorded videos, for improving game strategy and planning team lineups.
Banking & Finance
Deep learning has effectively prevented financial losses and secured customer portfolios. Machine learning algorithms built using deep learning are used for anomaly detection, behavioral analysis, and predictive modeling to detect and prevent fraudulent activities.
After leveraging deep learning, banks can easily analyze large volumes of data from social media, market trends, news, etc., and identify patterns to make risk assessments for their clients. This allows banks and financial organizations to make informed decisions and avoid potential risks. Deep learning is widely used to analyze customer behavior and patterns from website or app interaction. This information is used to recommend personalized financial products and services as per customer requirements.
Retail & E-Commerce
Deep learning has improved overall customer experience and increased sales for eCommerce and retail businesses. Using deep learning algorithms, companies can analyze various types of consumer data such as purchase history, buying habits, and user preferences. Subsequently, they can then use this data to recommend personalized products.
Neural networks allow retailers to forecast product demand, study market trends, and monitor social media activity to meet future demands and avoid understocking or overstocking products, leading to increased efficiency and saving inventory costs.
As eCommerce is gaining popularity fraudulent purchases and stolen credit card information has become a major concern. Deep learning algorithms can safeguard retail and eCommerce businesses from such suspicious activities and take a proactive approach to maintaining safety protocols.
AutoNOMOUS Driving
One of the most applicable uses of deep learning is autonomous driving. Companies such as Tesla, have developed fully autonomous vehicles that can navigate through traffic, read road signals, avoid obstacles, etc. These algorithms are built using computer vision technology that utilizes a combination of cameras, LiDAR, radar, and sensors to gather real-time information from its environment. This data is processed using neural networks to make crucial automated decisions while driving such as steering, accelerating, and braking.
Deep learning is also transforming the way engineers design cars. They can use Generative Adversarial Networks (GANs) to generate various car designs based on specific standards such as aesthetics or aerodynamics. These GAN models are trained on large visual data sets of existing cars to generate desired results based on specific criteria.
Learn more: The Art of Data Annotation in Machine Learning
Education
Educational institutes are leveraging the deep learning technology to revolutionize how students learn and teachers teach in the classrooms. Deep learning sophisticated algorithms allow educators to create custom learning modules for individual students based on their learning styles and preferences. This personalized learning makes studying easier and improves academic performance.
Language learning and translation is another field where deep learning is making waves. Schools enroll students from diverse backgrounds and language barriers can always hinder communication and the learning process. With advancements in NLP, machine learning models can quickly and accurately translate texts from another language in real-time. These deep learning capabilities are making classrooms more inclusive and interactive for training a global workforce.
Grading and assessing students is another crucial and time-consuming process that is prone to errors. Deep learning assessment tools can automatically grade students based on scoring algorithms, allowing teachers to see real-time insights and identify areas for improvement.
Final Thoughts
Deep learning is not only transforming the autonomous driving industry, it is a powerful driving force behind innovations in various fields such as retail, eCommerce, sports, finance, education, and much more. Neural networks built on deep learning algorithms simply human processes, reduce costs, study market trends, and understand user behavior. However, training deep learning models requires a lot of data, time, and expertise. This is where DDD comes in, our humans-in-the-loop annotators can help you train large amounts of data with the highest accuracy rate.
