Efficient Multi-branch Segmentation Network for Situation Awareness in
  Autonomous Navigation
Guan-Cheng Zhou Chen Chengb Yan-zhou Chena
Real-time and high-precision situational awareness technology is critical for
autonomous navigation of unmanned surface vehicles (USVs). In particular,
robust and fast obstacle semantic segmentation methods are essential. However,
distinguishing between the sea and the sky is challenging due to the
differences between port and maritime environments. In this study, we built a
dataset that captured perspectives from USVs and unmanned aerial vehicles in a
maritime port environment and analysed the data features. Statistical analysis
revealed a high correlation between the distribution of the sea and sky and row
positional information. Based on this finding, a three-branch semantic
segmentation network with a row position encoding module (RPEM) was proposed to
improve the prediction accuracy between the sea and the sky. The proposed RPEM
highlights the effect of row coordinates on feature extraction. Compared to the
baseline, the three-branch network with RPEM significantly improved the ability
to distinguish between the sea and the sky without significantly reducing the
computational speed.