AI-102 Implement computer vision solutions Practice Question
A retail company uses Azure AI Vision to analyze shelf images for inventory management. They notice that the Object Detection model sometimes misses small items. What is the most effective way to improve detection of small objects?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Train a custom object detection model with annotated images that include small objects.
Training a custom object detection model with annotated images that include small objects directly improves the model's ability to detect them. Option A is wrong because preprocessing to remove background noise does not specifically target small object detection; the model may still miss small items. Option C is wrong because the Background Removal API is used for isolating items from the background, not for improving detection accuracy. Option D is wrong although higher resolution can help, it is not as effective as training a custom model with properly annotated small objects, and it may increase cost and latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Preprocess images to remove background noise.
Why it's wrong here
Background removal does not directly improve detection of small objects.
- ✓
Train a custom object detection model with annotated images that include small objects.
Why this is correct
Custom training with representative data improves detection for specific scenarios.
- ✗
Use the Background Removal API to isolate items.
Why it's wrong here
Background Removal API is for segmentation, not object detection improvement.
- ✗
Increase the image resolution before sending to the API.
Why it's wrong here
Higher resolution may improve detection but is not a targeted solution for small objects.
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