What's in the Black Box? the False Negative Mechanisms Inside Object Detectors

Dimity Miller, Peyman Moghadam, Mark Cox, Matt Wildie, Raja Jurdak

Research output: Contribution to journalArticlepeer-review

Abstract

In object detection, false negatives arise when a detector fails to detect a target object. To understand why object detectors produce false negatives, we identify five 'false negative mechanisms,' where each mechanism describes how a specific component inside the detector architecture failed. Focusing on two-stage and one-stage anchor-box object detector architectures, we introduce a framework for quantifying these false negative mechanisms. Using this framework, we investigate why Faster R-CNN and RetinaNet fail to detect objects in benchmark vision datasets and robotics datasets. We show that a detector's false negative mechanisms differ significantly between computer vision benchmark datasets and robotics deployment scenarios. This has implications for the translation of object detectors developed for benchmark datasets to robotics applications.

Original languageEnglish
Pages (from-to)8510-8517
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume7
Issue number3
DOIs
StatePublished - Jul 1 2022
Externally publishedYes

Keywords

  • Deep learning for visual perception
  • object detection
  • segmentation and categorization

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