Maritime Obstacle Detection

Maritime obstacle detection focuses on enabling autonomous surface and aerial vehicles to safely navigate by accurately identifying obstacles in diverse aquatic environments. Current research emphasizes developing robust and computationally efficient deep learning models, including transformer-based architectures, for both segmentation and detection tasks, often incorporating temporal context to mitigate issues like reflections and sun glare. This field is crucial for advancing autonomous navigation systems, improving safety, and enhancing efficiency in maritime operations. The availability of large, diverse datasets and publicly accessible benchmarks is driving progress and fostering collaboration within the research community.