Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic Detection of Injection and Press Mold Parts on 2D Drawing
  Using Deep Neural Network   This paper proposes a method to automatically detect the key feature parts in
a CAD of commercial TV and monitor using a deep neural network. We developed a
deep learning pipeline that can detect the injection parts such as hook, boss,
undercut and press parts such as DPS, Embo-Screwless, Embo-Burring, and EMBO in
the 2D CAD drawing images. We first cropped the drawing to a specific size for
the training efficiency of a deep neural network. Then, we use Cascade R-CNN to
find the position of injection and press parts and use Resnet-50 to predict the
orientation of the parts. Finally, we convert the position of the parts found
through the cropped image to the position of the original image. As a result,
we obtained detection accuracy of injection and press parts with 84.1% in AP
(Average Precision), 91.2% in AR(Average Recall), 72.0% in AP, 87.0% in AR, and
orientation accuracy of injection and press parts with 94.4% and 92.0%, which
can facilitate the faster design in industrial product design.

36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
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The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin