mlsanityData to 7 Oct 2026

Humans disagree with the IoU for measuring object detector localization error

arXiv 2207.14221 ↗First posted 28 Jul 2022cs.CV cs.HCcited by 4

Not on the map. Why?

Abstract (arXiv)

The localization quality of automatic object detectors is typically evaluated by the Intersection over Union (IoU) score. In this work, we show that humans have a different view on localization quality. To evaluate this, we conduct a survey with more than 70 participants. Results show that for localization errors with the exact same IoU score, humans might not consider that these errors are equal, and express a preference. Our work is the first to evaluate IoU with humans and makes it clear that relying on IoU scores alone to evaluate localization errors might not be sufficient.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 13 of its 30 references that are in the map. First posted on arXiv 28 Jul 2022.

Builds on

13 of 30 references.

References · 13

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  3. 2017Mask R-CNNcited by 7,259
  4. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  5. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  6. 2015Fast R-CNNcited by 2,930
  7. 2018YOLOv3: An Incremental Improvementcited by 2,652
  8. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,161
  9. 2019Libra R-CNN: Towards Balanced Learning for Object Detectioncited by 255
  10. 2016We don't need no bounding-boxes: Training object class detectors using only human verificationcited by 20
  11. 2021Hallucination In Object Detection -- A Study In Visual Part Verificationcited by 16
  12. 2015Robot In a Room: Toward Perfect Object Recognition in Closed Environmentscited by 6
1 more
  1. 2022Evaluating Context for Deep Object Detectorscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 1.

4 papers.

  1. 2023Rethinking Intersection Over Union for Small Object Detection in Few-Shot Regimecited by 3
  2. 2024Do Object Detection Localization Errors Affect Human Performance and Trust?cited by 0
  3. 2024Aligning Object Detector Bounding Boxes with Human Preferencecited by 0
  4. 2026MicroscopyMatching: Towards a Ready-to-use Framework for Microscopy Image Analysis in Diverse Conditionscited by 0