Semantic segmentation is a computer vision technique used to partition an image into regions, where each region corresponds to a specific class or category. Unlike image classification, which assigns a single label to an entire image, semantic segmentation classifies every pixel in the image, effectively creating a detailed map that shows where different objects or regions are located. For example, in a photo of a street scene, semantic segmentation can label each pixel as belonging to a car, road, pedestrian, building, sky, or other relevant classes.
This fine-grained labeling helps machines understand the content and context of images at a much deeper level. Semantic segmentation is widely used in autonomous driving (for identifying roads, lanes, and obstacles), medical imaging (to highlight tumors or organs in scans), satellite imagery analysis, robotics, agriculture, and many other fields where understanding the precise location and boundaries of objects is crucial.
The process typically involves deep learning models—especially convolutional neural networks (CNNs)—that are trained on large, labeled datasets. These models learn to recognize patterns and textures associated with different classes. During inference, the model outputs a pixel-wise classification map, often visualized as a color-coded overlay that shows which part of the image belongs to which class.
It is important to note that semantic segmentation labels all objects of the same type with the same class label. So, if there are three dogs in the image, the model will label all pixels corresponding to any dog as “dog.” This is different from instance segmentation, which not only identifies the class but also distinguishes between different objects of the same class.
Evaluating the performance of semantic segmentation models often relies on metrics like Intersection over Union (IoU), which compares the overlap between the predicted and ground truth regions for each class. High-quality annotations, also known as golden datasets, are essential for training and evaluating these models accurately.
Recent advancements in semantic segmentation include the use of transformer-based architectures and multimodal data, which have further improved accuracy and robustness. As the field evolves, semantic segmentation continues to play a pivotal role in enabling machines to perceive and interact with the world in a more nuanced and intelligent way.
