품질 검사 퀴즈의 AI

퀴즈를 풀면 정답과 설명이 나타납니다.

1. What is one practical difference between AI-assisted inspection and human inspection?
   • A model can apply a consistent learned or programmed check across many images, but it still needs validation and oversight
   • AI guarantees detection of every defect
   • Humans cannot inspect images
   • AI removes the need for quality standards
2. 결함이 드물 때 이상 탐지가 유용한 이유는 무엇입니까?
   • 먼저 가능한 모든 결함의 예가 필요합니다.
   • '정상'이 어떤 것인지 학습하고 편차가 있는 모든 항목에 플래그를 지정합니다.
   • 이미지가 아닌 텍스트에서만 작동합니다.
   • 희귀한 사건을 완전히 무시합니다.
3. What do the cited visual-inspection benchmarks evaluate?
   • Only OCR accuracy on scanned contracts
   • Speech diarization and transcription quality
   • Unsupervised anomaly detection and localization on MVTec datasets
   • The accuracy of a single human inspector
4. 픽셀 수준 분할 마스크는 검사자에게 무엇을 말합니까?
   • 해당 제품의 판매가격
   • 컨베이어가 얼마나 빨리 움직이는지
   • 공장 주소
   • Pixels that the model predicts as anomalous and that an inspector may need to verify
5. What deployment measurement should a factory team validate before placing an anomaly model on an inspection line?
   • Latency and throughput on the target hardware, image resolution, and pipeline configuration
   • A guaranteed zero-millisecond response on any device
   • A universal speed number independent of the line
   • A benchmark score that proves every production image is an anomaly
6. What challenging conditions does the MVTec AD 2 paper include in its benchmark?
   • Only clean, uniformly lit images with large defects
   • Examples include transparent or overlapping objects, lighting changes, and very small defects
   • Only audio recordings from industrial machines
   • Only synthetic images with no real photographs
7. What average AU-PRO did evaluated methods achieve on the MVTec AD 2 paper’s benchmark?
   • Exactly 100% for every method
   • Above 99% for all methods
   • Below 60% on average in the reported evaluation
   • The paper reports only speech word-error rates
8. Why can benchmark performance fail to transfer to a factory line?
   • Benchmarks always contain every defect type
   • The model cannot process pixels
   • Factory images are always identical
   • Real line conditions and defect distributions may differ from the benchmark dataset