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Improving Visual Road Condition Assessment by Extensive Experiments on the Extended GAPs Dataset.

, , , , and . IJCNN, page 1-8. IEEE, (2019)

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How to get pavement distress detection ready for deep learning? A systematic approach., , , , , , , , and . IJCNN, page 2039-2047. IEEE, (2017)Increasing the Robustness of 2D Active Appearance Models for Real-World Applications., , and . ICVS, volume 5815 of Lecture Notes in Computer Science, page 364-373. Springer, (2009)Einsatz von Deep Learning zur automatischen Detektion und Klassifikation von Fahrbahnschäden aus mobilen LiDAR-Daten / Deep Learning for Automatic Detection and Classification ofRoad Damage from Mobile LiDAR Data., , and . AGIT Journal Angew. Geoinformatik, (2019)Efficient Implementation of Regional Mutual Information for the Registration of Road Images., , and . IPTA, page 1-6. IEEE, (2020)Road Surface Segmentation - Pixel-Perfect Distress and Object Detection for Road Assessment., , , , , , , , , and . CASE, page 1789-1796. IEEE, (2021)Improving Visual Road Condition Assessment by Extensive Experiments on the Extended GAPs Dataset., , , , and . IJCNN, page 1-8. IEEE, (2019)How to Improve Deep Learning based Pavement Distress Detection while Minimizing Human Effort., , , and . CASE, page 63-70. IEEE, (2018)MIRA - middleware for robotic applications., , , , and . IROS, page 2591-2598. IEEE, (2012)