38 citations · 65 across the 5 of their papers we have counts for
5 papers
Strategy to Increase the Safety of a DNN-based Perception for HAD Systems
Timo Sämann, Peter Schlicht, Fabian Hüger
Safety is one of the most important development goals for highly automated driving (HAD) systems. This applies in particular to the perception function driven by Deep Neural Networ…
MetaFusion: Controlled False-Negative Reduction of Minority Classes in Semantic Segmentation
Robin Chan, Matthias Rottmann, Fabian Hüger +2
In semantic segmentation datasets, classes of high importance are oftentimes underrepresented, e.g., humans in street scenes. Neural networks are usually trained to reduce the over…
Detection of False Positive and False Negative Samples in Semantic Segmentation
Matthias Rottmann, Kira Maag, Robin Chan +3
In recent years, deep learning methods have outperformed other methods in image recognition. This has fostered imagination of potential application of deep learning technology incl…
GAN- vs. JPEG2000 Image Compression for Distributed Automotive Perception: Higher Peak SNR Does Not Mean Better Semantic Segmentation
Jonas Löhdefink, Andreas Bär, Nico M. Schmidt +3
The high amount of sensors required for autonomous driving poses enormous challenges on the capacity of automotive bus systems. There is a need to understand tradeoffs between bitr…
Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation
Robin Chan, Matthias Rottmann, Fabian Hüger +2
As part of autonomous car driving systems, semantic segmentation is an essential component to obtain a full understanding of the car's environment. One difficulty, that occurs whil…