/tags/robotic-boat/ Recent content in Robotic Boat on ViCoS Lab Hugo en-us /publications/zust2022learning/ Mon, 01 Jan 0001 00:00:00 +0000 /publications/zust2022learning/ <p>Coastal water autonomous boats rely on robust perception methods for obstacle detection and timely collision avoidance. The current state-of-the-art is based on deep segmentation networks trained on large datasets. Per-pixel ground truth labeling of such datasets, however, is labor-intensive and expensive. We observe that far less information is required for practical obstacle avoidance &ndash; the location of water edge on static obstacles like shore and approximate location and bounds of dynamic obstacles in the water is sufficient to plan a reaction. We propose a new scaffolding learning regime (SLR) that allows training obstacle detection segmentation networks only from such weak annotations, thus significantly reducing the cost of ground-truth labeling. Experiments show that maritime obstacle segmentation networks trained using SLR substantially outperform the same networks trained with dense ground truth labels. Thus accuracy is not sacrificed for labelling simplicity but is in fact improved, which is a remarkable result.</p> /publications/bovcon2021wasr/ Mon, 01 Jan 0001 00:00:00 +0000 /publications/bovcon2021wasr/ <p>Obstacle detection using semantic segmentation has become an established approach in autonomous vehicles. However, existing segmentation methods, primarily developed for ground vehicles, are inadequate in an aquatic environment as they produce many false positive (FP) detections in the presence of water reflections and wakes. We propose a novel deep encoder-decoder architecture, a water segmentation and refinement (WaSR) network, specifically designed for the marine environment to address these issues. A deep encoder based on ResNet101 with atrous convolutions enables the extraction of rich visual features, while a novel decoder gradually fuses them with inertial information from the inertial measurement unit (IMU). The inertial information greatly improves the segmentation accuracy of the water component in the presence of visual ambiguities, such as fog on the horizon. Furthermore, a novel loss function for semantic separation is proposed to enforce the separation of different semantic components to increase the robustness of the segmentation. We investigate different loss variants and observe a significant reduction in false positives and an increase in true positives (TP). Experimental results show that WaSR outperforms the current state-of-the-art by approximately 4% in F1-score on a challenging USV dataset. WaSR shows remarkable generalization capabilities and outperforms the state of the art by over 24% in F1 score on a strict domain generalization experiment.</p>