Home/AI/Instance Segmentation/Introduction

🎯 Instance Segmentation

Detect and delineate individual object instances in images

Your Progress

0 / 5 completed
←
Previous Module
Semantic Segmentation

What is Instance Segmentation?

🎯 Definition

Instance segmentation combines object detection and semantic segmentation to detect and delineate each individual object instance. Unlike semantic segmentation which treats all objects of the same class as one, instance segmentation distinguishes between separate instances.

💡
Key Insight

Each object gets its own mask and label, even if multiple objects belong to the same class. Perfect for counting objects or tracking individuals.

🚗
Autonomous Driving

Identify each vehicle, pedestrian, and cyclist separately

🏥
Medical Imaging

Segment individual cells, tumors, or organs in scans

🤖
Robotics

Manipulate specific objects in cluttered environments

📊 Comparison with Other Tasks

📦

Object Detection

Bounding boxes around objects

Output: Box coordinates + class labels
🎨

Semantic Segmentation

Pixel-level classification, all instances merged

Output: Pixel class map (no instance distinction)
🎯

Instance Segmentation

Separate mask for each object instance

Output: Individual masks + class labels + boxes

✅ Advantages

  • •Distinguishes individual instances
  • •Enables object counting and tracking
  • •Provides precise object boundaries
  • •Works with overlapping objects

⚠️ Challenges

  • •Computationally expensive
  • •Requires instance-level annotations
  • •Difficult with occlusions
  • •Slower inference than detection