This paper introduces the Budding Ensemble Architecture (BEA), a novel reduced ensemble architecture for anchor-based object detection models. Object detection models are crucial in vision-based tasks, particularly in autonomous systems. They should provide precise bounding box detections while also calibrating their predicted confidence scores, leading to higher-quality uncertainty estimates. However, current models may make erroneous decisions due to false positives receiving high scores or true positives being discarded due to low scores. BEA aims to address these issues. The proposed loss functions in BEA improve the confidence score calibration and lower the uncertainty error, which results in a better distinction of true and false positives and, eventually, higher accuracy of the object detection models. Both Base-YOLOv3 and SSD models were enhanced using the BEA method and its proposed loss functions. The BEA on Base-YOLOv3 trained on the KITTI dataset results in a 6% and 3.7% increase in mAP and AP50, respectively. Utilizing a well-balanced uncertainty estimation threshold to discard samples in real-time even leads to a 9.6% higher AP50 than its base model. This is attributed to a 40% increase in the area under the AP50-based retention curve used to measure the quality of calibration of confidence scores. Furthermore, BEA-YOLOV3 trained on KITTI provides superior out-of-distribution detection on Citypersons, BDD100K, and COCO datasets compared to the ensembles and vanilla models of YOLOv3 and Gaussian-YOLOv3.

翻译：本文提出萌芽集成架构（BEA），一种用于锚点目标检测模型的新型简化集成架构。目标检测模型在基于视觉的任务中至关重要，尤其是在自主系统中。这类模型需提供精确的边界框检测，同时校准其预测的置信度分数，从而获得更高质量的不确定性估计。然而，当前模型可能因误判而做出错误决策——假阳性获得高分或真阳性因低分被丢弃。BEA旨在解决这些问题。BEA中提出的损失函数改进了置信度分数校准，降低了不确定性误差，从而更好地区分真阳性与假阳性，最终提升目标检测模型的准确率。使用BEA方法及其提出的损失函数对基础YOLOv3和SSD模型进行了增强。在KITTI数据集上训练的BEA增强基础YOLOv3，其mAP和AP50分别提升6%和3.7%。利用平衡良好的不确定性估计阈值实时剔除样本，甚至使AP50比基础模型高出9.6%。这归因于基于AP50的保留曲线下面积（用于衡量置信度分数校准质量）增加了40%。此外，在KITTI上训练的BEA-YOLOv3在Citypersons、BDD100K和COCO数据集上的分布外检测表现优于YOLOv3、高斯YOLOv3的集成模型及原始模型。