Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A water-obstacle separation and refinement network for unmanned surface
  vehicles   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.

24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat