Daniel Hofstätter | netidee

intro

Daniel Hofstätter

Projekte & Rollen

Increasing Trustworthiness of Edge AI by adding Uncertainty Estimation to Object Detection

Blogbeiträge

Base object detection vs uncertainty estimation approaches

YOLO with Uncertainty Estimation

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

mAP SSD vs SSD-EDL

Current State and Next Steps

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...

mAP for SSD EDL Beta

Evidential Deep Learning Tested on the Single-Shot Detector

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.

Active Learning Cycle Using Uncertainty Estimation

Continual and Active Learning: A Use Case for Uncertainty Estimation in Object Detection

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.

Precision-Recall curve showing mean average precision for object detection

Evaluating the Quality of Uncertainty Estimation

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.