Object Detection Models – An Overview
Until now, we focused on models that answer a simple question: “What is present in the image?”
Object Detection answers a harder and more useful question: “What objects are present, where are they located, and what are they?”
This lesson builds a strong foundation for understanding YOLO, SSD, Faster R-CNN, and modern detection systems.
What Is Object Detection?
Object Detection is a Computer Vision task that combines classification and localization.
For every object in an image, the model must:
- Identify the object class (car, person, dog, etc.)
- Draw a bounding box around the object
- Assign confidence to the prediction
Unlike image classification, multiple objects can exist in one image.
How Object Detection Is Different from Image Classification
| Task | Output | Example |
|---|---|---|
| Image Classification | Single label | “This image contains a dog” |
| Object Detection | Multiple labels + boxes | “Dog here, person there” |
Detection models must understand both what and where.
Key Components of Object Detection
Every object detection model predicts three things:
- Bounding Box: location of the object
- Class Label: what the object is
- Confidence Score: how sure the model is
Bounding boxes are usually represented as:
- (x, y, width, height)
- or (x_min, y_min, x_max, y_max)
Bounding Boxes Explained Simply
A bounding box is a rectangle drawn tightly around an object.
Good detection models:
- Place boxes accurately
- Avoid overlapping duplicate boxes
- Ignore background noise
Poor boxes reduce real-world usability.
Traditional vs Deep Learning Detection
Earlier detection methods used handcrafted features. Modern systems rely on deep learning.
| Approach | Technique | Limitations |
|---|---|---|
| Traditional | HOG + Sliding Window | Slow, less accurate |
| Deep Learning | CNN-based detectors | Needs more data |
All modern object detectors are CNN-based.
Two Main Categories of Detection Models
Object detection models fall into two broad categories.
1. Two-Stage Detectors (Accuracy First)
These models work in two steps:
- Step 1: Propose candidate object regions
- Step 2: Classify and refine bounding boxes
Examples:
- R-CNN
- Fast R-CNN
- Faster R-CNN
Strengths:
- High accuracy
- Precise localization
Weakness:
- Slower inference speed
2. One-Stage Detectors (Speed First)
These models detect objects in a single forward pass.
They directly predict:
- Bounding boxes
- Class probabilities
Examples:
- YOLO family
- SSD
- RetinaNet
Strengths:
- Very fast
- Real-time capable
Weakness:
- Slightly lower accuracy (historically)
Accuracy vs Speed Trade-off
There is always a trade-off.
Two-stage models → Higher accuracy, slower speed
One-stage models → Faster speed, slightly less precision
Modern YOLO models have reduced this gap significantly.
Why Object Detection Is Hard
Detection is challenging because:
- Objects vary in size and shape
- Multiple objects overlap
- Lighting and background vary
- Small objects are difficult to detect
Good detectors learn scale, context, and spatial relationships.
Where Object Detection Is Used
- Autonomous driving
- Surveillance systems
- Retail analytics
- Medical imaging
- Robotics and drones
Any system that must “see and react” uses detection.
Evaluation Metrics for Detection
Accuracy alone is not enough.
Common metrics:
- Intersection over Union (IoU)
- Precision and Recall
- mAP (mean Average Precision)
We will study these in detail in later lessons.
Practice Questions
Q1. What makes object detection harder than image classification?
Q2. Name one two-stage and one one-stage detector.
Q3. Why are one-stage detectors preferred for real-time systems?
Mini Assignment
Think about a real-world problem and decide:
- Is accuracy more important or speed?
- Would you choose a one-stage or two-stage model?
This decision-making skill is critical in interviews and projects.
Quick Recap
- Object detection finds what and where
- Bounding boxes localize objects
- Two-stage models prioritize accuracy
- One-stage models prioritize speed
- Detection powers many real-world systems
Next lesson: YOLO Basics – How Real-Time Detection Works.