Semantic Segmentation is a specific type of image segmentation technique used in computer vision to assign a label or category to each individual pixel in an image. It is detailed image labeling that helps the computer to understand the content of the image in more detail. Semantic Segmentation is a subcategory of image segmentation, alongside instance and panoptic segmentations.

What is Semantic Segmentation

Semantic segmentation identifies each pixel within an image and assigns a label to each one using a deep learning algorithm according to its characteristics. Semantic segmentation breaks down the image pixel by pixel, allowing computers to identify the image content at a very granular level. For example, in a mammogram, it can segment the breast tissue and identify abnormalities, like suspicious lesions within it, enabling radiologists to detect breast cancer.

Semantic segmentation is deployed by many applications like self-driving cars, medical image diagnosis, industrial inspection, aerial image processing, etc.

How does Semantic Segmentation Work?

Semantic segmentation models analyse the original image, like an image street scene, and create a segmentation map where each pixel is assigned a unique colour code based on its category in the input image. Segmentation masks refer to distinct coloured portions of the image. For example, a street scene might have three segmentation masks for ground (Gray), sky (blue), and trees (green).

Semantic segmentation models employ complex neural networks to group similar pixels into segmentation masks and correctly identify the real-world category associated with each group of pixels or segment. Semantic segmentation models need to be trained on a massive amount of image data already labeled by human labelers. The model analyses the labeled data and adjusts its parameters, such as weights and biases, using back propagation and gradient descent machine learning algorithms.

Accurate image classification for semantic segmentation necessitates data consisting of pixel-wise labels identifying different objects or classes within an image to train the model. Because of the complexity involved in image segmentation, typically such datasets are larger and more complex compared to other types of training data used for machine learning.

Semantic Segmentation Use Cases

Because of pixel-level annotations, semantic segmentation is more precise than other forms of object detection. As a result, semantic segmentation has several applications in computer vision across various industries that require precise understanding of image maps, including:

Self-driving Cars

Autonomous vehicles, like self-driving cars, integrate semantic segmentation techniques to classify individual objects and features like pedestrians, traffic lights, signs, buildings, trees, other vehicles, intersections, etc., around them, enabling them to take appropriate actions promptly. Semantic segmentation enables the self-driving car to take appropriate actions for safe navigation. This includes braking to avoid running into the vehicle in front or hitting pedestrians and adjusting to unexpected events like obstacles on the road or sudden lane changes

Background Removal

In addition to object identification, semantic segmentation can be used to create a mask for the background area in an image and remove it for specific tasks.

Medical Image Segmentation

Image analysis is an essential step in many common medical procedures, such as MRIs, CT scans, X-rays, and ultrasounds to diagnose conditions. AI-powered image segmentation models and semantic segmentation techniques can be utilized to analyze these images, helping medical professionals in diagnosis by identifying and outlining various objects in images. Semantic segmentation models can analyze medical images and draw boundaries around objects of interest like organs, bones, tissues, lesions, and fractures. This aids medical professionals in detecting medical issues and can potentially diagnose diseases.

Robotic Vision

The pixel-level accuracy provided by semantic segmentation significantly boosts robots’ ability to see and understand their surroundings. This enables them to perform visually demanding tasks that are challenging to interpret with simple vision, such as navigating through complex environments, differentiating between fragile items from heavy objects, and avoiding collisions effectively.

Farming

The semantic segmentation technique enables farmers to analyze images of their crops, captured using cameras or drones, to accurately detect any signs of pests or diseases. This helps agriculture professionals in identifying healthy pants and determining parts of a field that are infected. Accordingly, pesticides can be applied in a targeted manner, reducing pesticide use and environmental impact, as well as costs.

Fashion/ Virtual Try-on

Computer vision semantic segmentation makes virtual try-on possible, allowing users to digitally simulate wearing clothes, makeup, or accessories before making a purchase. Models trained on a massive dataset of images understand a user’s body and create a realistic visualisation of how these items would appear on him without visiting the physical store.

Wrapping It Up

Semantic segmentation’s ability to identify and differentiate each object within an image, pixel by pixel, is a game-changer in image classification. Neural networks leveraged by semantic segmentation algorithms facilitate the identification and differentiation of objects in an image, increasing real-world applications across various fields, as discussed above.

Seeing the World in Pixels: The Real-World Applications of Semantic Segmentation was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.