We demonstrate that many detection methods are designed to identify only a
sufficently accurate bounding box, rather than the best available one. To
address this issue we propose a simple and fast modification to the existing
methods called Fitness NMS. This method is tested with the DeNet model and
obtains a significantly improved MAP at greater localization accuracies without
a loss in evaluation rate, and can be used in conjunction with Soft NMS for
additional improvements. Next we derive a novel bounding box regression loss
based on a set of IoU upper bounds that better matches the goal of IoU
maximization while still providing good convergence properties. Following these
novelties we investigate RoI clustering schemes for improving evaluation rates
for the DeNet wide model variants and provide an analysis of localization
performance at various input image dimensions. We obtain a MAP of 33.6%@79Hz
and 41.8%@5Hz for MSCOCO and a Titan X (Maxwell). Source code available from:
this https URL

通过引入 Fitness NMS 和基于 IoU 的 bounding box 回归损失，结合 RoI 聚类，改进了 DeNet 的图像目标检测，显著提升了 Mean Average Precision（MAP），在 MSCOCO 数据集中 MAP 可达 33.6%@79Hz 和 41.8%@5Hz。