5 Most Crucial Applications of Deep Learning for Computer Vision

Deep learning, machine learning, and artificial intelligence are fields that are always growing and changing. It has set the stage for a new future with the notable increase in its use of deep learning for computer vision and related technologies throughout the business spectrum. Automated vehicles and friendly robots are becoming real and impacting our lives; they are no longer just characters in stories.

Applications of deep learning for computer vision in the real world are growing, which will expand the market for it. As a result, a CAGR of 33.5% is predicted for the period between 2023 and 2030.

The following are some of the catalysts for the development of deep learning and related technologies:

• Utilizing cloud-based technology rapidly
• Technological developments in data centers
• The capacity to carry out activities without requiring human involvement

New advancements are being made daily in the deep learning market. We have carefully chosen a few exceptional but interesting deep learning for computer vision applications for this blog.

What is Deep Learning: Overview

Deep learning is a specialized field within machine learning that leverages neural networks as its foundational subset. Its algorithms are developed and designed with inspiration from the human brain. Building relationships inside datasets is their main goal. In addition to helping computers recognize trends and patterns, deep learning algorithms also resolve challenging AI and ML issues.

Deep Learning is a machine learning approach based on layer-by-layer Artificial Neural Networks. A predetermined number of neurons make up each layer. These neurons take in input, process it, and then send the results to the layer after that until they reach the last layer. Except for the input layer, which is the first layer, and the output layer, which is the last layer, all the layers in a deep learning system are typically hidden.

Top 5 Applications of Deep Learning for Computer Vision

The advancement of deep learning technology has made it possible to build computer vision models that are more intricate and accurate. The integration of computer vision applications is becoming increasingly beneficial as these technologies advance. Here are the 5 most crucial applications of deep learning for computer vision.

1.     Object Detection

Object detection in computer vision includes two primary approaches.

• Two-step detection involves a Region Proposal Network (RPN) generating candidate regions, followed by neural classification using RCNN-based algorithms or ROI pooling in Fast RCNN, yielding high accuracy but slower performance.
• One-step detection, such as YOLO, SSD, and RetinaNet, integrates detection and classification by regressing bounding box predictions, enabling faster, real-time processing.

2.               Face Recognition

The fundamental ideas behind object and face recognition are still the same. The distinction is that attention is now drawn to the specifics required to recognize a human face in a picture or video. A large database of faces is employed for that reason. The face's contour, the space between the eyes, the form of the ears and cheekbones, and other characteristics are among the details that the algorithm examines.

The trickiest aspect of the process is trying to identify the same character in various lighting situations, from different perspectives, or when wearing glasses or a mask. Presently, a low-dimensional representation of 3D faces is being taught to convolutional neural networks so that classifiers can base their predictions on it. Compared to using 2D photos, this method may yield higher accuracy and faster operation than straightforward 3D recognition.

3.               Semantic Segmentation

Object segmentation, or semantic segmentation, is comparable to object detection but relies on the pixels associated with an object. This eliminates the need for boundary boxes and allows image objects to be more precisely specified. Typically, U-Nets or fully convolutional networks (FCN) are used for semantic segmentation.

Semantic segmentation is widely used in autonomous vehicle training. Researchers can utilize images of streets or throughways with clearly defined object boundaries.

4.               Motion Detection

Any surveillance system must have motion detection. This may be used to send someone a notification, set off an alarm, or just capture the event and store it for further analysis. Using a motion detector, which recognizes variations between image sequence frames, is one method of detecting motion. Thresholding is the most basic method of motion detection. This technique places a threshold value on each pixel in the frame and checks to see if the pixel has moved from its initial value to the point where it can be deemed to have changed considerably.

One can also use edge detection algorithms for motion detection. Once edges have been identified in an image, pixels that have been tagged as distinct from adjacent pixels are identified by edge detection methods.

5.               Recommendation Systems

Developing recommendation systems that make product recommendations to customers based on their prior behavior is one of the many ways that deep learning for computer vision benefits the industry. If you've ever wondered why your favorite music and video content keeps appearing on streaming services like Netflix, Spotify, and YouTube, the answer is deep learning. They pick up lessons from your behavior, the things you never look at, and the things you choose to hear or listen to.

However deep learning isn't limited to streaming services when it comes to its use in recommendation systems. Social media platforms such as Facebook and TikTok employ a comparable strategy to recommend pertinent posts, videos, and profiles to users inside their news feeds.

Wrapping Up

To address a multitude of practical issues and streamline a variety of procedures in the fields of healthcare, supply chain, transportation, manufacturing, and other areas, deep learning for computer vision is a very promising field. The field is growing, and the following are some of the most promising directions to pursue:

• Incorporating an object or words into a picture,
• Deleting things from photos that aren't necessary, and
• Scaling up pictures

NextGen Invent provides custom computer vision software development services, leveraging advanced deep learning and machine learning technologies. Reach out to us to explore how we can help achieve your objectives.