Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Det

Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection

This paper presents a novel alternative to Greedy Non-Maxima Suppression (NMS) in the task of bounding box selection and suppression in object detection. It proposes Confluence, an algorithm which does not rely solely on individual confidence scores to select optimal bounding boxes, nor does it rely on Intersection Over Union (IoU) to remove false positives.Using Manhattan Distance, it selects the bounding box which is closest to every other bounding box within the cluster and removes highly confluent neighboring boxes. Thus, Confluence represents a paradigm shift in bounding box selection and suppression as it is based on fundamentally different theoretical principles to Greedy NMS and its variants. Confluence is experimentally validated on RetinaNet, YOLOv3 and Mask-RCNN, using both the MS COCO and PASCAL VOC 2007 datasets. Confluence outperforms Greedy NMS in both mAP and recall on both datasets, using the challenging 0.50:0.95 mAP evaluation metric. On each detector and dataset, mAP was improved by 0.3-0.7% while recall was improved by 1.4-2.5%. A theoretical comparison of Greedy NMS and the Confluence Algorithm is provided, and quantitative results are supported by extensive qualitative results analysis. Furthermore, sensitivity analysis experiments across mAP thresholds support the conclusion that Confluence is more robust than NMS.

融合：对象检测中非最大值抑制的可靠非IoU替代方案

本文提出了一种在目标检测中的边界框选择和抑制任务中替代贪婪非极大值抑制（NMS）的新颖方法。它提出了Confluence，一种不仅仅依赖于个人置信度得分来选择最佳边界框的算法，也不依赖于联合交叉口（IoU）来消除误报。.. 使用“曼哈顿距离”，它选择最接近群集中其他所有边界框的边界框，并删除高度融合的相邻框。因此，Confluence基于与Greedy NMS及其变体根本不同的理论原理，代表着边界框选择和抑制的范式转变。使用MS COCO和PASCAL VOC 2007数据集，在RetinaNet，YOLOv3和Mask-RCNN上对融合进行了实验验证。使用具有挑战性的0.50：0.95 mAP评估指标，汇合在两个mAP上均优于Greedy NMS，并且在两个数据集上的召回率均优于。在每个检测器和数据集上，mAP改善了0.3-0.7％，而召回率则提高了1.4-2.5％。提供了Greedy NMS和Confluence算法的理论比较，广泛的定性结果分析支持定量结果。此外，跨mAP阈值的灵敏度分析实验支持以下结论：Confluence比NMS更可靠。 （阅读更多）