Semantic Segmentation and Its Applications


Semantic segmentation is the process of tagging each pixel in an image with a label. This is in stark contrast to classification, in which the entire image is assigned a single label. Multiple objects of the same class are treated as a single entity by semantic segmentation. Instance segmentation, on the other hand, treats multiple objects belonging to the same class as distinct individual objects (or instances). Typically, instance segmentation is harder than semantic segmentation.

This blog discusses what is semantic segmentation and what are its application.

Applications of Segmenting Instances

Semantic segmentation is applied in a variety of real-world situations. The following are some notable applications of semantic segmentation.

Autonomous Driving

Semantic segmentation is used to categorize lanes, vehicles, people, and other interesting objects. The resulting data is used to make intelligent decisions that will properly guide the vehicle.

One constraint on autonomous vehicles is the requirement for real-time performance. A solution to the aforementioned issue is to integrate a GPU into the vehicle on-board. To improve the performance of the preceding solution, either lighter (low parameter) neural networks or techniques for fitting neural networks on the edge can be used.

Medical Image Segmentation

Semantic segmentation is used to extract information from medical scans. It is particularly beneficial for detecting abnormalities such as tumors. Algorithm accuracy and low recall are critical for these applications.

Additionally, we can automate less critical operations such as estimating organ volume from 3D semantically segmented scans.

Recognize the Scene

Typically, semantic segmentation serves as the foundation for more complex tasks such as scene comprehension and visual question and answer (VQA). Typically, scene understanding algorithms produce a scene graph or caption.

Fashion Industry

In the fashion industry, semantic segmentation is used to extract clothing items from images in order to provide similar suggestions from retail stores. Advanced algorithms are capable of "redressing" specific items of clothing in an image.

Image Processing via Satellite (Or Aerial)

Semantic segmentation is a technique for classifying land types in satellite imagery. Segmenting water bodies to provide accurate map information is a common use case. Other advanced use cases include road mapping, identifying crop types, and locating available parking spaces.

Conclusion

Deep Learning algorithms for semantic segmentation have been significantly enhanced and simplified, paving the way for increased adoption in real-world applications. The concepts discussed in this blog are not exhaustive, as research communities are constantly working to improve these algorithms' accuracy and real-time performance. Nonetheless, this blog discusses several popular variants of these algorithms as well as their real-world applications.

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