Bounding Box Stability against Feature Dropout Reflects Detector
  Generalization across Environments
Yang Yang Wenhai Wang Zhe Chen Jifeng Dai Liang Zheng
Bounding boxes uniquely characterize object detection, where a good detector
gives accurate bounding boxes of categories of interest. However, in the
real-world where test ground truths are not provided, it is non-trivial to find
out whether bounding boxes are accurate, thus preventing us from assessing the
detector generalization ability. In this work, we find under feature map
dropout, good detectors tend to output bounding boxes whose locations do not
change much, while bounding boxes of poor detectors will undergo noticeable
position changes. We compute the box stability score (BoS score) to reflect
this stability. Specifically, given an image, we compute a normal set of
bounding boxes and a second set after feature map dropout. To obtain BoS score,
we use bipartite matching to find the corresponding boxes between the two sets
and compute the average Intersection over Union (IoU) across the entire test
set. We contribute to finding that BoS score has a strong, positive correlation
with detection accuracy measured by mean average precision (mAP) under various
test environments. This relationship allows us to predict the accuracy of
detectors on various real-world test sets without accessing test ground truths,
verified on canonical detection tasks such as vehicle detection and pedestrian
detection. Code and data are available at https://github.com/YangYangGirl/BoS.