Instance segmentation is a sophisticated task in computer vision that involves identifying and segmenting each individual object instance in an image at the pixel level. Unlike object detection, which only draws bounding boxes around objects, or semantic segmentation, which labels all pixels of the same class without distinguishing between different instances, instance segmentation provides both the class label and a precise contour for every object. This allows the model to differentiate between multiple objects of the same class (e.g., detecting and outlining several people in a crowd) with pixel-level accuracy.
To perform instance segmentation, modern approaches often combine object detection and semantic segmentation techniques. One of the most well-known models for this task is Mask R-CNN, which extends Faster R-CNN by adding a branch that predicts segmentation masks for each detected object. Applications of instance segmentation are found in autonomous vehicles (to differentiate between road users), medical imaging (to separate individual cells or tumors), and robotics (for precise object manipulation). Despite being computationally demanding, instance segmentation provides a rich understanding of visual scenes, making it crucial for tasks that require detailed object-level analysis.