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Learn Detection Computer Vision Tutorial, validate concepts with Detection Computer Vision MCQ Questions, and prepare interviews through Detection Computer Vision Interview Questions and Answers.
Object Detection Intro MCQ
From image classification to localizing objects with boxes: IoU matching, precision–recall, and the idea of proposals vs dense prediction.
Box
x,y,w,h
IoU
Overlap
mAP
PR curve
Dense / sparse
Proposals
Object detection basics
Detectors output class scores and bounding boxes (and sometimes masks or keypoints). Training needs matching predictions to ground truth—IoU is the usual overlap criterion.
mAP
Average precision integrates precision–recall across score thresholds; mAP averages over classes (and IoU thresholds in COCO).
Vocabulary
IoU
Intersection area divided by union of two axis-aligned boxes (for AABB case).
TP / FP
Matched high-IoU prediction to an unmatched GT is TP; overlap wrong class or duplicate is FP.
Sliding window
Classify every location/scale—expensive; modern nets predict boxes densely or from proposals.
Anchor / prior boxes
Template boxes regressed to objects—sizes/aspect ratios cover the dataset prior.
Output
List of (class, score, box) per detected object