AI-900 Practice Question: Describe features of computer vision workloads on Azure
An autonomous drone needs to navigate a forest by identifying individual trees, including their exact shape and boundaries, to avoid colliding with branches. The drone also needs to distinguish between trees and other objects like rocks. Which Azure Computer Vision capability is best suited for this requirement?
⚠ Common exam trap
Watch out — candidates often confuse object detection (bounding boxes) with semantic segmentation (pixel-level masks), assuming bounding boxes provide enough detail for precise boundary avoidance, but the question explicitly requires 'exact shape and boundaries,' which only pixel-level segmentation can deliver.
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
✓
Semantic segmentation
Semantic segmentation is the correct choice because it classifies every pixel in an image, assigning each pixel to a specific class (e.g., 'tree', 'rock', 'branch'). This pixel-level precision allows the drone to identify the exact shape and boundaries of individual trees, which is essential for collision avoidance in a forest environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Image classification
Why it's wrong here
Image classification assigns a single global label to the entire image, such as 'forest', but provides no spatial localization. A drone needs to know precisely where trees, branches, and clear gaps are located within the scene, not just that the scene is a forest. Without pixel-level or region-level geometry, the drone cannot compute safe collision-avoidance paths around irregular obstacles.
- ✗
Object detection
Why it's wrong here
Object detection goes further than classification by locating objects with rectangular bounding boxes and labeling them, for example drawing a box around a tree. However, bounding boxes are axis-aligned rectangles that inherently include surrounding background or exclude protruding branches and concave shapes. For navigating dense vegetation, the drone needs the exact silhouette of each obstacle, so a coarse rectangular approximation is insufficient for safe path planning.
- ✓
Semantic segmentation
Why this is correct
Semantic segmentation assigns a class label to every pixel in the image, producing a dense, pixel-accurate mask that outlines the exact boundaries of trees, rocks, terrain, and sky. This per-pixel understanding gives the drone the precise shape and location of all obstacles, enabling it to identify navigable gaps and calculate collision-free trajectories. It is the most appropriate computer vision technique for dense, unstructured outdoor environments like a forest.
- ✗
Optical character recognition (OCR)
Why it's wrong here
Optical character recognition (OCR) is designed to detect and extract text characters from images, such as letters on signs or labels. It does not model the visual structure or geometry of natural objects, so it cannot distinguish a tree trunk from foliage or a rock from the ground. In a forest navigation task where the drone must identify and avoid physical obstacles, OCR provides no useful spatial or semantic information about the environment.
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