mlsanityData to 14 Oct 2026

Explaining YOLO: Leveraging Grad-CAM to Explain Object Detections

arXiv 2211.12108 ↗First posted 22 Nov 2022cs.CV cs.AIcited by 6

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Abstract (arXiv)

We investigate the problem of explainability for visual object detectors. Specifically, we demonstrate on the example of the YOLO object detector how to integrate Grad-CAM into the model architecture and analyze the results. We show how to compute attribution-based explanations for individual detections and find that the normalization of the results has a great impact on their interpretation.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 6 of its 9 references that are in the map. First posted on arXiv 22 Nov 2022.

Builds on

6 of 9 references.

References · 6

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2018YOLOv3: An Incremental Improvementcited by 2,659
  4. 2018Deep Learning for Generic Object Detection: A Surveycited by 392
  5. 2018Learning to Explain: An Information-Theoretic Perspective on Model Interpretationcited by 248
  6. 2020Black-box Explanation of Object Detectors via Saliency Mapscited by 76

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Cited by year (2026 to 14 Oct)

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

6 papers.

  1. 2023Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainabilitycited by 11
  2. 2025Deep Unfolding Multi-modal Image Fusion Network via Attribution Analysiscited by 7
  3. 2023You Only Explain Oncecited by 2
  4. 2024On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial Applicationscited by 2
  5. 2024ProtoP-OD: Explainable Object Detection with Prototypical Partscited by 1
  6. 2026Reconstructed holograms and explanation-aware evaluation for low-cost computational pollen analysis in veterinary cytologycited by 0