  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.
