YOLO Real-Time Detection Quiz

Answers and explanations appear as you take the quiz.

1. What does the acronym YOLO stand for in this context?
   • You Only Look Once
   • You Only Learn Once
   • Your Object Locator Online
   • Yielded Object Label Output
2. Which cross-domain evaluation did the original YOLO paper report?
   • Transcribing speech from noisy recordings
   • Generalizing from natural images to artwork datasets
   • Classifying satellite images with no labels
   • Estimating depth from stereo video only
3. What technique removes duplicate, overlapping bounding boxes in YOLO's output?
   • Batch normalization
   • Dropout
   • Non-maximum suppression
   • Data augmentation
4. In the original YOLO design, how is the input image divided for prediction?
   • Into random crops
   • Into color channels
   • Into a single region
   • Into a grid of cells
5. Which quantity does each grid cell predict alongside bounding box coordinates?
   • An objectness confidence score and class probabilities
   • The image resolution
   • The camera focal length
   • The file compression ratio
6. Why is YOLO especially popular for video and live camera applications?
   • It cannot run on GPUs
   • Its single-pass speed enables real-time frame rates
   • It is the only model that uses color
   • It requires no training at all
7. What accuracy tradeoff did the original YOLO paper report relative to leading detection systems?
   • It had no localization errors but missed every small object
   • It could detect only one class
   • It made more localization errors while being less likely to predict false detections where nothing existed
   • It required thousands of region-wise classifier runs
8. Why can a real-time detector still be unsuitable for a particular deployment?
   • A real-time model cannot process images
   • Frame rate guarantees safety certification
   • A detector’s speed proves every object is found
   • Its measured speed does not establish adequate accuracy, latency or robustness for that setting