  Robust maritime obstacle detection is essential for fully autonomous unmanned
surface vehicles (USVs). The currently widely adopted segmentation-based
obstacle detection methods are prone to misclassification of object reflections
and sun glitter as obstacles, producing many false positive detections,
effectively rendering the methods impractical for USV navigation. However,
water-turbulence-induced temporal appearance changes on object reflections are
very distinctive from the appearance dynamics of true objects. We harness this
property to design WaSR-T, a novel maritime obstacle detection network, that
extracts the temporal context from a sequence of recent frames to reduce
ambiguity. By learning the local temporal characteristics of object reflection
on the water surface, WaSR-T substantially improves obstacle detection accuracy
in the presence of reflections and glitter. Compared with existing single-frame
methods, WaSR-T reduces the number of false positive detections by 41% overall
and by over 53% within the danger zone of the boat, while preserving a high
recall, and achieving new state-of-the-art performance on the challenging MODS
maritime obstacle detection benchmark. The code, pretrained models and extended
datasets are available at https://github.com/lojzezust/WaSR-T
