2020/05/14 by Philipp Oberdiek, Oberdiek, Philipp, Matthias Rottmann +3
Computer Science · Engineering · #Automated Road and Building Extraction #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Traffic Prediction and Management Techniques #Vehicle emissions and performance #cs.CV
paper · pdf · doi:10.48550/arxiv.2005.06831
arxiv created 2020/05/14 · openalex publication_date 2020/05/14 · arxiv updated 2020/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
When deploying deep learning technology in self-driving cars, deep neural networks are constantly exposed to domain shifts. These include, e.g., changes in weather conditions, time of day, and long-term temporal shift. In this work we utilize a deep neural network trained on the Cityscapes dataset containing urban street scenes and infer images from a different dataset, the A2D2 dataset, containing also countryside and highway images. We present a novel pipeline for semantic segmenation that detects out-of-distribution (OOD) segments by means of the deep neural network's prediction and performs image retrieval after feature extraction and dimensionality reduction on image patches. In our experiments we demonstrate that the deployed OOD approach is suitable for detecting out-of-distribution concepts. Furthermore, we evaluate the image patch retrieval qualitatively as well as quantitatively by means of the semi-compatible A2D2 ground truth and obtain mAP values of up to 52.2%.