The current expirimental setup will be reworked from its foundation by switching from the SSD model to the latest YOLO11 object detector.

Current experiments showed severe degradations in performance due to a combination of limitations in object detection architecture, training routine, and uncertainty estimation methods. This blog post will highlight these and suggest the next directi...

Evidential Deep Learning (EDL) for classification was added to the Single-Shot Detector (SSD). However, the evaluation shows a significant loss in precision. Therefore, further implementation approaches will be discussed.

Uncertainty estimation helps models to identify difficult inputs, which can be further used for continual learning, enabling models to evolve. Active learning prioritizes valuable data by utilizing uncertainty as a metric, reducing labeling costs.

When adding uncertainty estimation to object detection, we need to make sure that these new modifications do not degrade the performance of the base model. Therefore, we evaluate the Precision-Recall curve and its mean Average Precision.