  In the majority of object detection frameworks, the confidence of instance
classification is used as the quality criterion of predicted bounding boxes,
like the confidence-based ranking in non-maximum suppression (NMS). However,
the quality of bounding boxes, indicating the spatial relations, is not only
correlated with the classification scores. Compared with the region proposal
network (RPN) based detectors, single-shot object detectors suffer the box
quality as there is a lack of pre-selection of box proposals. In this paper, we
aim at single-shot object detectors and propose a location-aware anchor-based
reasoning (LAAR) for the bounding boxes. LAAR takes both the location and
classification confidences into consideration for the quality evaluation of
bounding boxes. We introduce a novel network block to learn the relative
location between the anchors and the ground truths, denoted as a localization
score, which acts as a location reference during the inference stage. The
proposed localization score leads to an independent regression branch and
calibrates the bounding box quality by scoring the predicted localization score
so that the best-qualified bounding boxes can be picked up in NMS. Experiments
on MS COCO and PASCAL VOC benchmarks demonstrate that the proposed
location-aware framework enhances the performances of current anchor-based
single-shot object detection frameworks and yields consistent and robust
detection results.
