Current top performing object detectors employ detection proposals to guide
the search for objects, thereby avoiding exhaustive sliding window search
across images. Despite the popularity and widespread use of detection
proposals, it is unclear which trade-offs are made when using them during
object detection. We provide an in-depth analysis of twelve proposal methods
along with four baselines regarding proposal repeatability, ground truth
annotation recall on PASCAL, ImageNet, and MS COCO, and their impact on DPM,
R-CNN, and Fast R-CNN detection performance. Our analysis shows that for object
detection improving proposal localisation accuracy is as important as improving
recall. We introduce a novel metric, the average recall (AR), which rewards
both high recall and good localisation and correlates surprisingly well with
detection performance. Our findings show common strengths and weaknesses of
existing methods, and provide insights and metrics for selecting and tuning
proposal methods.

该论文通过分析 12 种提案方法及 4 种基线，评估了它们对物体识别精度、重复性以及物体本地化准确性的影响，并提出了一个新的指标 —— 平均召回率，该指标同时奖励高召回率和良好的本地化准确性，并与检测性能惊人地相关。该论文的发现为选择和调整提案方法提供了指导和度量标准。