ブラックボックスの中身：物体検出器内部の偽陰性メカニズム
What's in the Black Box? The False Negative Mechanisms Inside Object Detectors
物体検出器が偽陰性を生む内部要因を5つに分類し、Faster R-CNNとRetinaNetで定量化する枠組みを提案。ベンチマークとロボティクス現場で失敗要因が大きく異なることを示した。
著者: Dimity Miller, Peyman Moghadam, Mark Cox, Matt Wildie, Raja Jurdak
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
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. Code is publicly available at https://github.com/csiro-robotics/fn_mechanisms
