Confidence Score: The Forgotten Dimension of Object Detection Performance Evaluation

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ID: 267562
2021
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Abstract
When deploying a model for object detection, a confidence score threshold is chosen to filter out false positives and ensure that a predicted bounding box has a certain minimum score. To achieve state-of-the-art performance on benchmark datasets, most neural networks use a rather low threshold as a high number of false positives is not penalized by standard evaluation metrics. However, in scenarios of Artificial Intelligence (AI) applications that require high confidence scores (e.g., due to legal requirements or consequences of incorrect detections are severe) or a certain level of model robustness is required, it is unclear which base model to use since they were mainly optimized for benchmark scores. In this paper, we propose a method to find the optimum performance point of a model as a basis for fairer comparison and deeper insights into the trade-offs caused by selecting a confidence score threshold.
Reference Key
wenkel2021sensorsconfidence Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Simon Wenkel;Khaled Alhazmi;Tanel Liiv;Saud Alrshoud;Martin Simon;Wenkel, Simon;Alhazmi, Khaled;Liiv, Tanel;Alrshoud, Saud;Simon, Martin;
Journal sensors
Year 2021
DOI
10.3390/s21134350
URL
https://www.mdpi.com/1424-8220/21/13/4350
Keywords
deep neural networks computer vision object detection confidence score

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