人工智能质检测验

当您参加测验时，就会出现答案和解释。

1. AI 辅助检测与人工检测之间的一个实际区别是什么？
   • 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. 当缺陷很少时，为什么异常检测很有用？
   • 它首先需要每个可能的缺陷的示例
   • 它了解“正常”是什么样子并标记任何异常情况
   • 它仅适用于文本，不适用于图像
   • 它完全忽略了罕见事件
3. 引用的视觉检测基准评估什么？
   • 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. 像素级分割掩模告诉检查员什么？
   • 产品的销售价格
   • 输送机移动的速度有多快
   • 工厂地址
   • Pixels that the model predicts as anomalous and that an inspector may need to verify
5. 工厂团队在将异常模型部署到检测线上之前，应验证哪些部署测量？
   • 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