Summary of Measuring the Impact Of Scene Level Objects on Object Detection: Towards Quantitative Explanations Of Detection Decisions, by Lynn Vonder Haar et al.
Measuring the Impact of Scene Level Objects on Object Detection: Towards Quantitative Explanations of Detection Decisions
by Lynn Vonder Haar, Timothy Elvira, Luke Newcomb, Omar Ochoa
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel black box explainability method for object detection models to gain insight into their decision-making processes. Traditional accuracy metrics lack depth and may not reveal how features are learned by the model. This approach focuses on the impact of scene-level objects, such as buildings or people, on the detection of specific objects within an image. The study employs a fine-tuned YOLOv8 model to assess the effect of these scene-level objects on emergency road vehicle detection. By comparing accuracy with and without scene-level objects, the method provides a quantitative explanation of the model’s decision process, enhancing our understanding of its performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how object detection models work. We usually just look at how accurate they are, but that doesn’t show us why they make certain decisions. The authors suggest a new way to check if an object detection model is relying on certain things in the background of the image, like buildings or people, to identify objects. They use a specific type of model called YOLOv8 and test it by comparing how well it detects emergency vehicles with and without these background elements. This will help us better understand why the model makes certain decisions. |
Keywords
* Artificial intelligence * Object detection
