Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Objectness Scoring and Detection Proposals in Forward-Looking Sonar
  Images with Convolutional Neural Networks   Forward-looking sonar can capture high resolution images of underwater
scenes, but their interpretation is complex. Generic object detection in such
images has not been solved, specially in cases of small and unknown objects. In
comparison, detection proposal algorithms have produced top performing object
detectors in real-world color images. In this work we develop a Convolutional
Neural Network that can reliably score objectness of image windows in
forward-looking sonar images and by thresholding objectness, we generate
detection proposals. In our dataset of marine garbage objects, we obtain 94%
recall, generating around 60 proposals per image. The biggest strength of our
method is that it can generalize to previously unseen objects. We show this by
detecting chain links, walls and a wrench without previous training in such
objects. We strongly believe our method can be used for class-independent
object detection, with many real-world applications such as chain following and
mine detection.

12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
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The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph