Semantic segmentation is a subcategory of computer vision, a field of computer science that ‘trains’ computers to process and interpret images the same way that a human brain does. Computer vision (CV), at first, had very basic functions, and could only identify basic elements such as lines and curves. A deeper understanding of images came later when artificial intelligence (AI) and its subsets like machine learning and deep learning improved.
Deep learning, in particular, had been very effective in dealing with images as data. In some cases, it even outperforms humans. The trickiest parts that scientists encountered were in image classification, object detection, and semantic segmentation. These three are now among the key applications of computer vision and image processing that enabled these technologies to be used in many industries.
An Overview Of Semantic Segmentation
Semantic segmentation consists of categorizing the pixels in images, then labeling them. The pixels would be linked to a class. This means that every pixel found in an image is paired with the correct classification. So, semantic segmentation considers various objects that belong to the same class as one entity.
Take for example an image of three dogs on a sofa. Semantic segmentation would attach a label to each pooch as ‘dog.’ Then, the pixels that make up the image of each dog are again categorized and labeled further as ‘dog.’
Semantic segmentation has two main tasks: classification and detection. ‘Classifying’ means you’re grouping an image in a similar category. ‘Detection,’ meanwhile, means localizing and identifying an object. A deeper explanation of semantic segmentation is published here.
How Various Industries Benefit From Semantic Segmentation
Semantic segmentation, under computer vision, is used in many industries because it helps AI to have a profound understanding of an image. It classifies and detects an image at the pixel level, which gives machines with computer vision a more accurate image perception.
Below is a list of industries that benefit from semantic segmentation and how they benefit from it:
Retail and Ecommerce
Virtual fitting rooms are now possible, due to semantic segmentation’s ability to accurately classify clothes. An online retail store can let you try on clothes without physically visiting a brick-and-mortar shop, and you also don’t have to change clothes.
You could also experiment with various make-up products, and get personal recommendations from the AI. A few retail stores also use in-store virtual fitting rooms, where a touchscreen display using CV can re-create a virtual image of you wearing various clothing based on images from the shop’s catalog. Some stores even use facial recognition for their regular customers to offer them customized product recommendations or even discounts.
Personalized user experience and advertisement inspire customers to continue patronizing the shop and increase sales. It’s one of the most effective digital marketing strategies. Customers will also have a more rewarding digital experience. Additionally, human interactions are also minimized in stores which, given the current situation, means reducing health risks. This also means lower labor costs.
Medical Industry
Patients are easily recognized by cameras with CV, preventing procedures from being done on wrong patients. Analysis of medical images is also improved, helping healthcare professionals interpret X-ray, MRIs, and CT scans. Semantic segmentation, through CV, can also assist medical professionals to accurately diagnose some cases of cancer by examining images of cellular structures.
The technology also helps surgeries to be more precise, through surgical simulation that assists surgeons by giving a detailed guide to complex surgical procedures. An algorithm is also used by CV to detect COVID-19, called COVID-Net that has a 90% accuracy rate. Semantic segmentation also helps to identify the bacteria present in smears, improving the speed and accuracy of diagnostic tests.
Automotive Industry
Artificial intelligence used in autonomous or self-driving vehicles depends on semantic segmentation to make these vehicles safe for everybody. An autonomous vehicle should be able to perceive the road and its environment reliably as if a person is on the wheel. The technology can analyze visual scenes through the camera, identifying relevant objects such as traffic signs, pedestrians, other vehicles, sidewalks, roads, structures, and others.
It interprets images with object types, giving meaningful labels such as pedestrian, car, stop sign, and similar things. The cars can also detect whether an area is drivable or not. Semantic segmentation helps self-driving cars recognize and label road signs, making them navigate the roads properly and safely. The technology is crucial in making accidents involving autonomous vehicles that are extremely rare.
Conclusion
The advancement of semantic segmentation enabled computer vision to improve by leaps and bounds. It helped computer vision to process image data in a more meaningful and useful manner, resulting in the accurate interpretation and understanding of images and the environment.
As a result, semantic segmentation is now used by industries like retail, medical, and the automotive industry, giving people a more satisfying shopping experience, more accurate and faster diagnostic tests, and safer and more reliable autonomous vehicles.
