Applications of Deep Learning for Computer Vision

Computer vision technology powered by Deep Learning (DL) provides real-world value across industries. Such intelligent technologies have been around for a few years, and it’s finally coming of age and rising in prominence.

In fact, computer vision is precisely what makes driverless cars possible. However, there’s a myriad of possibilities and use cases, including the augmentation of human sight.

The primary objective here is to enable computers to process their environment and understand the world through sight. Whenever machines understand the world around them, they can navigate through it and make better decisions.

But before we discuss applications of deep learning in computer vision, let’s first define it.

Table of Contents

• Deep Learning Defined
• Computer Vision Defined
• Challenges for Computer Vision
• Role of Suitable Training Data
• Deep Learning-Powered Computer Vision in Healthcare
• Deep Learning-Powered Computer Vision in Retail
• Deep Learning-Powered Computer Vision in the Automotive Industry
• Conclusion
• FAQs

Deep Learning Defined

Deep Learning, or DL, is a type of Artificial Intelligence (AI) and Machine Learning (ML) that mimics how humans learn in certain situations. It’s also a critical element of data science, including predictive modeling and statistics.

There are three different types of DL and ML used to train algorithms:

1. Supervised learning
2. Unsupervised learning
3. Reinforcement learning

The goal here is to leverage intelligent algorithms to facilitate complete automation and minimize human intervention. As such, DL is at the heart of innovations that strive to achieve human-level performance or even try to do better than that.

Deep Learning

Computer Vision Defined

Computer vision is the field in AI that concentrates on enabling machines or computers to see. This means identifying and processing images just like humans do and provide an appropriate output.

In a way, it’s like you’re equipping a machine with human instincts and intelligence. However, it’s a massive challenge as it’s pretty difficult to get computers to recognize different images of objects and people.

Modern computer vision applications depend on the following capabilities and technologies:

• Object classification (to assign objects in videos or photographs)
• Object localization to locate an object within an image (by drawing bounding boxes around it)
• Semantic segmentation (to better understand every pixel and associate a class label to it)
• Instance segmentation for semantic segmentation (and identify multiple instances of the same class)

When companies successfully power machines with computer vision, the computer will correctly interpret what it sees, perform an analysis, and act accordingly.

Computer Vision

Challenges in Computer Vision

Despite recent considerable advancements in computer vision, there are still a number of fundamental problems that need to be resolved.

• Positioning of objects is one of them. AI must to be able to locate objects in addition to classifying them. To meet the needs of real-time video processing, algorithms also need to recognize objects very quickly.
• Weak planning for building the ML model that is deployed for the computer vision system may present another difficulty. Executives frequently set goals during the planning stage that are too lofty for the data science team to meet.
• Applications for computer vision use both software algorithms and hardware technologies in a two-pronged architecture (cameras and often IoT sensors). You have failure in implementation if the latter is improperly configured.

Role of Suitable Training Data

Regardless of the use case and the tools you use, the success of your application depends on the AI training datasets for machine learning. The better the data, the better the chance of developing a successful Deep learning-driven computer vision application.

In this case, the training data that intelligent algorithms use to learn must be all-inclusive, comprehensive, and representative of the planet we live on. It’s critical because the more machines can accurately recognize the world around them, the lower the chance of error.

Tip:

Training Data is available from clickworker in all quantities and in high quality to train your deep learning system optimally

More about Datasets for Machine Learning

Deep Learning-Powered Computer Vision in Healthcare

The healthcare sector has consistently been at the cutting-edge of technology. This approach helps the industry consistently innovate and provide better care for patients. So it’s no surprise to find computer vision technology in the healthcare vertical.

Healthcare computer vision has several use cases. These include COVID-19 diagnosis, cancer detections, cell classification, mask detection, and more.

For example, researchers at MIT were able to leverage deep convolutional neural networks and devise a system that quickly analyzes wide-field images of the patient’s skin to detect skin cancer efficiently.

Furthermore, as DL has seen considerable success in computer vision, it enables the automated processing of medical images. This approach helps doctors diagnose COVID-19 and better understand how the disease evolves.

Deep Learning-Powered Computer Vision in Retail

E-commerce giants like Amazon have consistently analyzed customer behavior on their platform for years. This approach helps the companies deliver enhanced user experiences.

Although physical retail stores have wanted to do the same and optimize in-store experiences, it wasn’t possible until now. Today, we have tools powered by DL and computer vision that automatically capture how customers interact with displayed items.

When used with face detection tools, intelligent algorithms can quickly evaluate the customer’s gender, age group, emotions, and more. When used together with footfall counters and security cameras, you can also track customer behavior within a store.

By being alert to dwell areas and browsing patterns, retailers can identify new opportunities to boost sales and revenue. The insights gained from this data may also lead management to rearrange the store, offer product recommendations, and more. Store owners can also use the same tools to track staff movement and productivity (for example, reassign staff to areas where they are needed the most).

Computer vision can also help enhance and optimize self-checkouts, real-time inventory management, and make recommendations using virtual mirrors (for example the Bourjois Magic Mirror).

Other benefits of computer vision in retail include:

• Discovering marketing and promotional opportunities (for example, in dwell areas)
• Enforcing of social distancing protocols
• Productivity analytics (tracking how staff spend their time and resources)
• Quality assurance and management
• Real-time theft detection
• Skills training
• Wait time analytics (including queue detection)

When all this comes together perfectly, you’ll have a high-performing store with satisfied customers.

Deep Learning-Powered Computer Vision in the Automotive Industry

Computer Vision in Automotive Industry

We can’t really talk about DL, ML, and computer vision without discussing the automotive industry. Companies have been working on autonomous vehicles for decades, but self-driving cars were far from reality until recently. Today, it’s probably the only application of computer vision that has received the most media attention.

Although autonomous cars have ML algorithms packed into them, it’s computer vision that makes safe driving possible. In this scenario, the “agent” algorithm that controls the motor vehicles is always aware of the car’s environment.

By “seeing” the road, other vehicles in the vicinity, and the distance between potential objects and obstacles, it’s able to make calculations and adapt to its continuously changing environment.

You can also find DL and computer vision in the transportation sector in the following AI-powered protocols:

• Automated license plate recognition
• Collision avoidance systems
• Distracted driving
• Driver attentiveness detection
• Infrastructure condition assessment
• Moving violations detection
• Parking occupancy detection
• Pedestrian detection
• Road condition monitoring
• Traffic flow analysis
• Traffic sign detection
• Vehicle classification
• Vehicle re-identification

Top tools used for computer vision include:

• Amazon Rekognition
• CUDA
• MATLAB
• OpenCV
• SimpleCV
• TensorFlow

Conclusion

A wide range of practical issues can be resolved and several processes in the fields of healthcare, sports, transportation, retail, manufacturing, etc. can be made simpler using deep learning for computer vision, which is an incredibly promising study field.

FAQs on Applications of Deep Learning for Computer Vision

What do modern computer vision applications depend on?

Modern computer vision applications depend on the following capabilities and technologies:

• Object classification (to assign objects in videos or photographs)
• Object localization to locate an object within an image (by drawing bounding boxes around it)
• Semantic segmentation (to better understand every pixel and associate a class label to it)
• Instance segmentation for semantic segmentation (and identify multiple instances of the same class)

What is the importance of proper training data?

The training data that intelligent algorithms use to learn must be all-inclusive, comprehensive, and representative of the planet we live on. It’s critical because the more machines can accurately recognize the world around them, the lower the chance of error.

How is Deep Learning and computer vision used in the transportation sector?

• Automated license plate recognition
• Collision avoidance systems
• Distracted driving
• Driver attentiveness detection
• Infrastructure condition assessment