Deep learning models rely on extensive image or video data to be successful. However, even a single image contains a wealth of information that models must accurately locate and recognize before making accurate predictions. This is where image segmentation techniques, specifically semantic image segmentation, play a crucial role.
In this article, we’ll discuss what is segmentation in image processing, what is semantic segmentation and its importance, and the various nuanced applications of semantic segmentation.
Businesses are increasingly investing in computer vision and machine learning models to garner a competitive edge in their market. But to ensure their machines will even work correctly, let alone give them an advantage over the competition, engineers must use millions of images and videos to create high-quality training datasets for their models. These files must then be broken down and categorized on a per-pixel level through a process called semantic image segmentation.
Image segmentation is a computer vision technique that divides an image into separate regions or segments based on various visual properties like color and texture to analyze further and understand an image’s content. This way, machine learning solutions and models can isolate, detect, or recognize objects in an image.
On the other hand, semantic image segmentation is a more advanced image segmentation technique of assigning semantic labels to each pixel in the image, providing a more granular level of detail. This level of granularity enhances the machine’s learning process and enables it to understand more complex elements, such as spatial relationships and boundaries between different objects in an image. For example, to train autonomous driving vehicles, parts of images are classified into vehicles, pedestrians, road dividers, and traffic signs.
Semantic image segmentation facilitates precise object localization and boundary delineation by providing pixel-level annotations, empowering machine learning models to make more accurate predictions and better understand visual data.
In the case of autonomous driving vehicles, computer vision semantic segmentation helps machines distinguish a pedestrian from a traffic sign, a road from a building, and a tree from a house to ensure road safety. This technique is crucial in creating effective computer vision models that rely heavily on training and testing data in machine learning.
The whole process involves an image segmentation convolutional neural network (CNN), a specific type of deep learning architecture designed for image segmentation. From preparation to output, here’s a quick overview of how semantic segmentation models work:
Semantic segmentation services empower various industries and businesses to precisely analyze and interpret visual data. Here are some key areas where semantic image segmentation is widely used.
For years, TaskUs has demonstrated its semantic segmentation expertise in supporting image and video annotation for autonomous driving companies.
One of our clients, a Globally Leading Autonomous Vehicle (AV) Company, partnered with Us to support them in exponentially scaling, refining, and enhancing their AI training through high-precision data image processing solutions.
The project started with 100 FTEs and rapidly grew to over 1,800 FTEs:
With Us, our client’s autonomous vehicles were able to map the roads faster as we doubled their overall production rate with our proactive approach to robust quality management.
Dubbed by the Everest Group as the World’s Fastest Growing Business Process (Outsourcing) Service Provider in 2022 and with glowing reviews in Gartner Peer Insights, TaskUs is a proven partner in image annotation outsourcing and web search evaluation services.
From deploying teams of image annotation experts to ensuring data security and maintaining meticulous quality checks–we’ve got every step covered to ensure your models’ and investment’s success.