Object Detection Basics
Until now, Computer Vision tasks were mostly about low-level understanding: pixels, edges, shapes, and patterns.
Object Detection is where vision becomes meaningful. Instead of asking “Where are the edges?”, we now ask:
- What objects are present?
- Where exactly are they located?
- How many objects exist?
This lesson builds the foundation for all modern detection systems used in real products.
What Is Object Detection?
Object Detection is the task of:
- Identifying objects in an image
- Drawing a bounding box around each object
- Assigning a class label to each box
So object detection answers three questions:
- What? (class)
- Where? (location)
- How many? (count)
Object Detection vs Image Classification
This distinction is extremely important.
| Task | What it does |
|---|---|
| Image Classification | Predicts a single label for the whole image |
| Object Detection | Finds multiple objects and their locations |
An image classification model might say:
“This image contains a car.”
An object detection model says:
“There are three cars here, and these are their positions.”
Bounding Boxes Explained
Each detected object is represented by a bounding box.
A bounding box usually includes:
- x-coordinate of top-left corner
- y-coordinate of top-left corner
- Width
- Height
This simple rectangle is enough to locate objects accurately for most applications.
Traditional Object Detection (Before Deep Learning)
Before deep learning, object detection relied on:
- Handcrafted features
- Sliding window approach
- Classifiers like SVM
The process was:
- Slide a window across the image
- Extract features from each window
- Classify each window
This approach was:
- Slow
- Computationally expensive
- Hard to scale
Why Object Detection Is Difficult
Object detection is harder than it looks because:
- Objects vary in size
- Objects overlap
- Lighting changes
- View angles differ
- Backgrounds are complex
A good detection system must handle all these variations reliably.
Modern Object Detection (Big Picture)
Modern object detection systems use deep learning.
The general idea:
- Use a CNN to extract features
- Predict bounding boxes
- Predict class probabilities
These models learn both what an object looks like and where it is located.
Two Main Detection Approaches
Modern detection models fall into two categories:
| Approach | Description |
|---|---|
| Two-stage detectors | First find regions, then classify (e.g., R-CNN) |
| One-stage detectors | Detect everything in one pass (e.g., YOLO) |
You will study both approaches in later lessons.
Real-World Applications
Object detection is used in:
- Self-driving cars (vehicles, pedestrians)
- Surveillance systems
- Retail checkout automation
- Medical imaging
- Robotics
Almost every AI-powered vision system relies on object detection.
Evaluation Metrics (Conceptual)
Detection models are evaluated using:
- IoU (Intersection over Union)
- Precision
- Recall
You will study these metrics in detail later, but remember:
Detection is not just about accuracy — location matters.
Where You Will Implement This
You will implement object detection using:
- OpenCV pre-trained detectors
- YOLO-based models
- Deep learning frameworks
Recommended practice environments:
- Google Colab (GPU support)
- Local Python + OpenCV
Practice Questions
Q1. What are the three outputs of an object detector?
Q2. Why is object detection harder than classification?
Q3. What is the purpose of bounding boxes?
Homework / Thinking Exercise
- Look at images with multiple objects
- Imagine bounding boxes around each object
- Think about overlaps and scale differences
This mental mapping helps a lot before coding.
Quick Recap
- Object detection finds and localizes objects
- Uses bounding boxes and class labels
- More complex than classification
- Foundation for modern vision systems
Next, you will explore Face Detection using Haar Cascades, which is a classic and important detection method.