Overview
Object detection is a key computer vision workload tested on the AI-900 exam. It goes beyond identifying what appears in an image by also determining where those objects are located. Object detection solutions analyze images (or video frames) and return labels, bounding boxes, and confidence scores.
On the AI-900 exam, you must be able to:
- Recognize object detection scenarios
- Distinguish object detection from image classification and image segmentation
- Identify Azure services that support object detection
What Is Object Detection?
Object detection is a computer vision technique that:
- Identifies multiple objects in an image
- Assigns labels to each object
- Returns bounding boxes showing object locations
It answers the question:
“What objects are in this image, and where are they?”
Key Characteristics of Object Detection
1. Bounding Boxes
- Objects are located using rectangular boxes
- Each bounding box defines:
- Position (x, y coordinates)
- Size (width and height)
This is the clearest differentiator from image classification.
2. Multiple Objects per Image
Object detection can:
- Detect multiple objects
- Identify different object types in the same image
Example:
- Person
- Bicycle
- Car
Each with its own bounding box.
3. Labels with Confidence Scores
For each detected object, the solution returns:
- A label (for example, Car)
- A confidence score indicating prediction certainty
4. Real-Time and Batch Use
Object detection can be used for:
- Real-time scenarios (video feeds, camera streams)
- Batch processing (analyzing stored images)
Common Object Detection Scenarios
Object detection is appropriate when location matters.
Typical Use Cases
- Counting people or vehicles
- Security and surveillance
- Retail analytics (products on shelves)
- Traffic monitoring
- Autonomous systems (identifying obstacles)
Object Detection vs Image Classification
Understanding this difference is critical for AI-900.
| Feature | Image Classification | Object Detection |
|---|---|---|
| Labels entire image | ✅ | ❌ |
| Identifies object locations | ❌ | ✅ |
| Uses bounding boxes | ❌ | ✅ |
| Detects multiple objects | ❌ | ✅ |
Exam Tip:
If a question mentions “count,” “locate,” “draw boxes,” or “find all”, object detection is the correct choice.
Azure Services for Object Detection
Azure AI Vision (Prebuilt Models)
- Provides ready-to-use object detection
- Detects common objects
- No training required
- Accessible via REST APIs
Azure AI Custom Vision
- Supports custom object detection models
- Requires:
- Labeled images
- Bounding box annotations
- Ideal for domain-specific objects
Features of Object Detection Solutions on Azure
Cloud-Based Inference
- Runs in Azure
- Scales automatically
- Accessible via APIs
Custom vs Prebuilt Models
- Prebuilt models for general use
- Custom models for specialized scenarios
Integration with Applications
- Can be embedded into:
- Web apps
- Mobile apps
- IoT solutions
- Often used with camera feeds or uploaded images
When to Use Object Detection
Use object detection when:
- You need to find and locate objects
- Multiple objects may exist
- You need counts or spatial awareness
When Not to Use It
- When only overall image labels are required
- When pixel-level accuracy is needed (segmentation)
Responsible AI Considerations
At a high level, AI-900 expects awareness of:
- Bias in training images
- Privacy when detecting people
- Transparency in how results are used
Key Exam Takeaways
- Object detection identifies what and where
- Uses bounding boxes + labels
- Supports multiple objects per image
- Azure AI Vision = prebuilt
- Azure AI Custom Vision = custom models
- Watch for keywords: detect, locate, count, bounding box
Go to the Practice Exam Questions for this topic.
Go to the AI-900 Exam Prep Hub main page.
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