TY - CHAP A1 - Behret, Valentino A1 - Kushtanova, Regina A1 - Fadl, Islam A1 - Weber, Simon A1 - Helmer, Thomas A1 - Palme, Frank ED - Bashford-Rogers, Thomas ED - Meneveaux, Daniel ED - Ammi, Mehdi ED - Ziat, Mounia ED - Jänicke, Stefan ED - Purchase, Helen ED - Radeva, Petia ED - Furnari, Antonino ED - Bouatouch, Kadi ED - Sousa, A. Augusto T1 - Sensor Calibration and Data Analysis of the MuFoRa Dataset T2 - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - (Volume 2) N2 - Autonomous driving sensors face significant challenges under adverse weather conditions such as fog and rain, which can seriously degrade their performance and reliability. Existing datasets often lack the reproducible and measurable data needed to adequately quantify these effects. To address this gap, a new multimodal dataset (MuFoRa) has been collected under controlled adverse weather conditions at the CARISSMA facility, using a stereo camera and two solid-state LiDAR sensors. This dataset is used to quantitatively assess sensor degradation by measuring the entropy for images and the number of inliers for point clouds on a spherical target. These metrics are used to evaluate the impact on performance under varying conditions of fog (5 to 150 m visibility) and rain (20 to 100 mm/h intensity) at different distances (5 to 50 m). Additionally, two calibration target detection approaches - Deep-learning and Hough-based - are evaluated to achieve accurate sensor alignment. The contributions include the introduction of a new dataset focused on fog and rain, the evaluation of sensor degradation, and an improved calibration approach. This dataset is intended to support the development of more robust sensor fusion and object detection algorithms for autonomous driving. UR - https://doi.org/10.5220/0013310400003912 Y1 - 2025 UR - https://doi.org/10.5220/0013310400003912 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58088 SN - 978-989-758-728-3 SP - 622 EP - 631 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Fadl, Islam A1 - Schön, Torsten A1 - Behret, Valentino A1 - Brandmeier, Thomas A1 - Palme, Frank A1 - Helmer, Thomas ED - Bashford-Rogers, Thomas ED - Meneveaux, Daniel ED - Ammi, Mehdi ED - Ziat, Mounia ED - Jänicke, Stefan ED - Purchase, Helen ED - Radeva, Petia ED - Furnari, Antonino ED - Bouatouch, Kadi ED - Sousa, A. Augusto T1 - Environment Setup and Model Benchmark of the MuFoRa Dataset T2 - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - (Volume 3) N2 - Adverse meteorological conditions, particularly fog and rain, present significant challenges to computer vision algorithms and autonomous systems. This work presents MuFoRa a novel, controllable, and measured multimodal dataset recorded at CARISSMA’s indoor test facility, specifically designed to assess perceptual difficulties in foggy and rainy environments. The dataset bridges research gap in the public benchmarking datasets, where quantifiable weather parameters are lacking. The proposed dataset comprises synchronized data from two sensor modalities: RGB stereo cameras and LiDAR sensors, captured under varying intensities of fog and rain. The dataset incorporates synchronized meteorological annotations, such as visibility through fog and precipitation levels of rain, and the study contributes a detailed explanation of the diverse weather effects observed during data collection in the methods section. The dataset’s utility is demonstrated through a baseline evaluation example, asse ssing the performance degradation of state-of-the-art YOLO11 and DETR 2D object detection algorithms under controlled and quantifiable adverse weather conditions. The public release of the dataset (https://doi.org/10.5281/zenodo.14175611) facilitates various benchmarking and quantitative assessments of advanced multimodal computer vision and deep learning models under the challenging conditions of fog and rain. UR - https://doi.org/10.5220/0013307900003912 Y1 - 2025 UR - https://doi.org/10.5220/0013307900003912 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58070 SN - 978-989-758-728-3 SP - 729 EP - 737 PB - SciTePress CY - Setúbal ER -