Stiftbild
Increasing Trustworthiness of Edge AI by Adding Uncertainty Estimation to Object Detection
Female avatar
Daniel Hofstätter

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

Förderjahr 2023 / Stipendien Call #18 / Stipendien ID: 6885

Object detection is a common machine learning task that can be used to detect various categories of entities, e.g., people, in images and mark them with bounding boxes and a classification score.

Still, typical models are not able to fully express the uncertainty in their predictions, hence it is usually not known when a model prediction is trustworthy or not. However, a model with uncertainty estimation is able to say ”I don't know“, when it encounters an input unlike anything seen before during training.

Challenges of uncertainty estimation typically are bigger model sizes and higher inference times, which are problematic for real-time applications at the resource-constrained Edge.

The goal of this thesis is to implement and evaluate different approaches of uncertainty estimation for object detection. Evaluation checks how well the model distinguishes between input data it has been trained to process, and out-of-distribution samples it does not have the capabilities to handle well.

Uni | FH [Universität]

Technische Universität Wien

Gesamtklassifikation

Diplomarbeit
,
Masterarbeit
Datenschutzinformation
Der datenschutzrechtliche Verantwortliche (Internet Privatstiftung Austria - Internet Foundation Austria, Österreich) würde gerne mit folgenden Diensten Ihre personenbezogenen Daten verarbeiten. Zur Personalisierung können Technologien wie Cookies, LocalStorage usw. verwendet werden. Dies ist für die Nutzung der Website nicht notwendig, ermöglicht aber eine noch engere Interaktion mit Ihnen. Falls gewünscht, treffen Sie bitte eine Auswahl: