论文标题
无人体表面车辆的水孔分离和改进网络
A water-obstacle separation and refinement network for unmanned surface vehicles
论文作者
论文摘要
语义分割的障碍物检测表明，无人体表面车辆（USV）的自动导航有很大的希望。但是，在存在视觉歧义的情况下，现有方法的水边缘估计不佳，对水反射和唤醒的高障碍物的检测不佳以及高阳性速率的较差。我们提出了一个新的深层编码器架构，一个水上的分离和改进网络（WASR），以解决这些问题。一种新型解码器提高了检测和水边缘精度，该解码器逐渐将IMU的惯性信息与编码器的视觉特征融合在一起。此外，新颖的损失函数旨在增加网络早期水和障碍物特征之间的分离。随后，更好地利用了解码器中其余层的能力，从而大大降低了假阳性并增加了真实的阳性。实验结果表明，WASR的表现优于当前的最新边际边缘，而第二好的方法的F量增加了14％。
Obstacle detection by semantic segmentation shows a great promise for autonomous navigation in unmanned surface vehicles (USV). However, existing methods suffer from poor estimation of the water edge in the presence of visual ambiguities, poor detection of small obstacles and high false-positive rate on water reflections and wakes. We propose a new deep encoder-decoder architecture, a water-obstacle separation and refinement network (WaSR), to address these issues. Detection and water edge accuracy are improved by a novel decoder that gradually fuses inertial information from IMU with the visual features from the encoder. In addition, a novel loss function is designed to increase the separation between water and obstacle features early on in the network. Subsequently, the capacity of the remaining layers in the decoder is better utilised, leading to a significant reduction in false positives and increased true positives. Experimental results show that WaSR outperforms the current state-of-the-art by a large margin, yielding a 14% increase in F-measure over the second-best method.
