Dr. rer. nat.

Cheng, Chih-Hong

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  • Publication
    Statistical Guarantees for Safe 2D Object Detection Post-processing
    ( 2023)
    Seferis, Emmanouil
    ;
    Burton, Simon
    ;
    Kollias, Stefanos
    ;
    Cheng, Chih-Hong
    Safe and reliable object detection is essential for safetycritical applications of machine learning, such as autonomous driving. However, standard object detection methods cannot guarantee their performance during operation. In this work, we leverage conformal prediction in order to provide statistical guarantees for back-box object detection models. Extending prior work, we present a postprocessing methodology that can cover the entire object detection problem (localization, classification, false negatives, detection in videos, etc.), while offering sound safety guarantees on its error rates. We apply our method on state-of-the-art 2D object detection models and measure its efficacy in practice. Moreover, we investigate what happens as the acceptable error rates are pushed towards high safety levels. Overall, the presented methodology offers a practical approach towards safety-aware object detection, and we hope it can pave the way for further research in this area.
  • Publication
    Formally Compensating Performance Limitations for Imprecise 2D Object Detection
    ( 2022-08-25)
    Schuster, Tobias
    ;
    Seferis, Emmanouil
    ;
    Burton, Simon
    ;
    Cheng, Chih-Hong
    In this paper, we consider the imperfection within machine learning-based 2D object detection and its impact on safety. We address a special sub-type of performance limitations related to the misalignment of bounding-box predictions to the ground truth: the prediction bounding box cannot be perfectly aligned with the ground truth. We formally prove the minimum required bounding box enlargement factor to cover the ground truth. We then demonstrate that this factor can be mathematically adjusted to a smaller value, provided that the motion planner uses a fixed-length buffer in making its decisions. Finally, observing the difference between an empirically measured enlargement factor and our formally derived worst-case enlargement factor offers an interesting connection between quantitative evidence (demonstrated by statistics) and qualitative evidence (demonstrated by worst-case analysis) when arguing safety-relevant properties of machine learning functions.