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-noise removal alters pixel statistics but leaves object scale unchanged, so small items remain below the detector's effective receptive-field resolution. Preprocessing is genuinely useful for cleaning low-contrast or cluttered captures, but the fix here is tiling or upscaling images so small objects occupy more pixels.
- ✓
Train a custom object detection model with annotated images that include small objects.
Why this is correct
Custom training with annotated images containing small objects teaches the model the specific visual features and scale variation needed, directly addressing missed detections. Generic pre-trained models lack this domain tuning, so retraining on representative shelf imagery is the effective remedy.
- ✗
Use the Background Removal API to isolate items.
Why it's wrong here
Background removal strips context and produces a segmentation mask, not improved localisation of tiny objects; the detector still receives the same downsampled scale. It is the right tool for compositing or privacy redaction, yet small-object recall improves through image tiling or higher-resolution input, not foreground isolation.
- ✗
Increase the image resolution before sending to the API.
Why it's wrong here
Upscaling images does not add pixel detail the model never captured; small objects remain small relative to the frame, so detection is unchanged. It is tempting because resolution intuitively aids vision, but the correct approach is training a custom model on labelled shelf images with small-object examples.
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