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 DL in computer vision, let’s first define it.
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.
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.
DL-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.
DL-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
Wait time analytics (including queue detection)
When all this comes together perfectly, you’ll have a high-performing store with satisfied customers.
DL-Powered Computer Vision in the 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
Driver attentiveness detection
Infrastructure condition assessment
Moving violations detection
Parking occupancy detection
Road condition monitoring
Traffic flow analysis
Traffic sign detection
Top tools used for computer vision include:
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 DL-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.
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