  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
