Student for thesis in the field of Out of Distribution Detection in Object Recognition

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Number: MER000403Q AI-based object recognition is a central component of perception systems in automated driving. Since these AI models are only trained on limited data sets, You can meet unknown objects or situations (out-of distribution, OOD) in real scenarios. Detection of such OOD inputs is an important component for the safety argumentation of automated systems, as it helps to detect insecure model predictions and initiate potential degradation maneuvers. In this work, various OOD methods are to be researched, implemented and examined in the application example of object recognition. These challenges come to you: Research and analysis of current scientific work on out-of-distribution (OOD) detection on the application example of object recognition Identification of suitable OOD methods and algorithms for existing object detection models Implementation of selected OOD algorithms and integration into an existing object recognition pipeline Conducting experiments to evaluate the methods on suitable data sets Comparison and analysis of the results with regard to detection performance and computational effort Documentation of results and derivation of recommendations for practical use

The final topics will be discussed with the university, you and us. The activity can begin from July 2026.

Registered student* in the field of engineering, computer science, mathematics or comparable Programming skills in Python Basic knowledge in machine learning and deep learning, ideally focusing on computer vision Secure knowledge of German and English in word and writing

Commitment and team skills Analytical thinking and strategic working

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