论文标题
强大的海洋障碍检测的时间上下文
Temporal Context for Robust Maritime Obstacle Detection
论文作者
论文摘要
强大的海上障碍物检测对于完全自动的无人体表面车辆（USV）至关重要。目前广泛采用的基于细分的障碍检测方法容易分类对象反射和阳光作为障碍，从而产生许多假阳性检测，从而有效地使USV导航的方法不切实际。但是，对物体反射的水扰动引起的时间外观变化与真实物体的外观动力学非常独特。我们利用这一属性来设计wasr-t，这是一个新型的海上障碍检测网络，从最近的一系列框架中提取时间上下文，以减少歧义。通过学习水面上对象反射的局部时间特征，WASR-T可以在存在反射和闪光的情况下显着提高障碍物检测精度。与现有的单帧方法相比，WASR-T将假阳性检测的数量降低了41％，在船的危险区域内将误报数量减少了53％，同时保留了高召回率，并在具有挑战性的Mod上实现了新的最先进的绩效海上牛头垒检测基准。代码，预估计的模型和扩展数据集可在https://github.com/lojzezust/wasr-t上获得
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
