Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Light-weight and Real-time Line Segment Detection   Previous deep learning-based line segment detection (LSD) suffers from the
immense model size and high computational cost for line prediction. This
constrains them from real-time inference on computationally restricted
environments. In this paper, we propose a real-time and light-weight line
segment detector for resource-constrained environments named Mobile LSD
(M-LSD). We design an extremely efficient LSD architecture by minimizing the
backbone network and removing the typical multi-module process for line
prediction found in previous methods. To maintain competitive performance with
a light-weight network, we present novel training schemes: Segments of Line
segment (SoL) augmentation, matching and geometric loss. SoL augmentation
splits a line segment into multiple subparts, which are used to provide
auxiliary line data during the training process. Moreover, the matching and
geometric loss allow a model to capture additional geometric cues. Compared
with TP-LSD-Lite, previously the best real-time LSD method, our model
(M-LSD-tiny) achieves competitive performance with 2.5% of model size and an
increase of 130.5% in inference speed on GPU. Furthermore, our model runs at
56.8 FPS and 48.6 FPS on the latest Android and iPhone mobile devices,
respectively. To the best of our knowledge, this is the first real-time deep
LSD available on mobile devices. Our code is available.

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The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
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The categories of this paper are: physics
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The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: bayes-an
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin