Enhancing Inland Water Safety: The Lake Constance Obstacle Detection Benchmark

Autonomous navigation on inland waters requires an accurate understanding of the environment in order to react to possible obstacles. Deep learning is a promising technique to detect obstacles robustly. However, supervised deep learning models require large data-sets to adjust their weights and to generalize to unseen data. Therefore, we equipped our research vessel with a laser scanner and a stereo camera to record a novel obstacle detection data-set for inland waters. We annotated 1974 stereo images and lidar point clouds with 3d bounding boxes. Furthermore, we provide an initial approach and a suitable metric2 to compare the results on the test data-set. The data-set is publicly available3 and seeks to make a contribution towards increasing the safety on inland waters.