A Survey on the Deployability of Semantic Segmentation Networks for Fluvial Navigation

Abstract

Neural network semantic image segmentation has developed into a powerful tool for autonomous navigational environmental comprehension in complex environments. While semantic segmentation networks have seen ample applications in the ground domain, implementations in the surface water domain, especially fluvial (rivers and streams) deployments, have lagged behind due to training data and literature sparsity issues. To tackle this problem the publicly available River Obstacle Segmentation En-Route By USV Dataset (ROSEBUD) was recently published. The dataset provides unique rural fluvial training data for the water binary segmentation task to aid in fluvial scene au-tonomous navigation. Despite new dataset sources, there is still a need for studies on networks that excel at both under-standing marine and fluvial scenes and efficiently operating on the computationally limited embedded systems that are common on autonomous marine platforms like ASVs. To provide insight into state-of-the-art network capabilities on embedded systems a survey of twelve networks encompassing 8 different architectures has been developed. Networks were trained and tested on a combination of three existing datasets, including the ROSEBUD dataset, and then implemented on an NVIDIA Jetson Nano to evaluate performance on real-world hardware. The survey's results layout recommendations for networks to use in autonomous applications in complex and fast-moving environments relative to network performance and inference speed.