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
this https URL

该研究通过对两种物体检测器的结构和数据集的分析，提出了 “五种假负面机制”，并探究了 Faster R-CNN 和 RetinaNet 在计算机视觉基准数据集和机器人应用中的 “假负面机制” 显著不同的原因。