Instance Segmentation – Separating Individual Objects
So far, you have learned how semantic segmentation labels every pixel. But it has one important limitation.
It does not distinguish between different instances of the same class.
Instance segmentation solves this problem by answering a more precise question:
Which pixel belongs to which object?
Why Semantic Segmentation Is Not Enough
Imagine an image with five people.
Semantic segmentation will label all of them as:
“person”
But it will not tell:
- Which pixels belong to person 1
- Which pixels belong to person 2
For many real-world problems, this is not sufficient.
What Is Instance Segmentation?
Instance segmentation assigns:
- A class label
- A unique object identity
- A pixel-accurate mask
Each object is treated as a separate instance, even if multiple objects belong to the same class.
Semantic vs Instance Segmentation
| Aspect | Semantic Segmentation | Instance Segmentation |
|---|---|---|
| Class labels | Yes | Yes |
| Object identity | No | Yes |
| Separates same-class objects | No | Yes |
| Mask per object | No | Yes |
How Instance Segmentation Thinks
Instance segmentation combines ideas from:
- Object detection
- Semantic segmentation
Conceptually, it works in three steps:
- Find objects (bounding boxes)
- Classify each object
- Create a pixel-level mask for each object
This makes it more complex than semantic segmentation.
Why Instance Segmentation Is Harder
Instance segmentation must solve:
- Overlapping objects
- Objects touching each other
- Different sizes and shapes
The model must understand:
- What is foreground vs background
- Where one object ends and another begins
Real-World Example
Consider a street scene:
- 10 cars
- 5 pedestrians
- 2 bicycles
Semantic segmentation gives:
- Car pixels
- Person pixels
- Bicycle pixels
Instance segmentation gives:
- Car #1, Car #2, …
- Person #1, Person #2, …
- Bicycle #1, Bicycle #2
Why Instance Segmentation Matters
Many applications require object-level understanding:
- Autonomous driving
- Robotics manipulation
- Medical image analysis
- Video tracking
Without instance segmentation, these systems fail.
Instance Segmentation vs Object Detection
Object detection draws boxes.
Instance segmentation goes further.
| Feature | Object Detection | Instance Segmentation |
|---|---|---|
| Bounding boxes | Yes | Yes |
| Pixel-level masks | No | Yes |
| Object separation | Partial | Accurate |
Popular Models for Instance Segmentation
Several architectures have been proposed:
- Mask R-CNN
- YOLACT
- Detectron-based models
Among these, Mask R-CNN became the most influential.
That is why the next lesson focuses entirely on it.
How Output Looks Conceptually
The output of instance segmentation includes:
- Bounding box
- Class label
- Binary mask for each object
Each object has its own mask, independent of others.
Common Mistakes Beginners Make
- Confusing instance segmentation with semantic segmentation
- Thinking bounding boxes are enough
- Ignoring overlapping objects
Understanding this distinction is critical for interviews and real projects.
Practice Questions
Q1. Why can’t semantic segmentation separate multiple people in an image?
Q2. What additional information does instance segmentation provide?
Q3. Which task combines object detection and segmentation?
Mini Assignment
Think of a supermarket shelf image.
- Why is instance segmentation better than detection?
- Why is semantic segmentation insufficient?
Answer conceptually.
Quick Recap
- Semantic segmentation labels pixels by class
- Instance segmentation separates objects of the same class
- Each object gets its own mask
- Used in autonomous driving, robotics, medicine
- Foundation for Mask R-CNN
Next lesson: Mask R-CNN – Instance Segmentation in Practice.