Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Object sorting using faster R-CNN   In a factory production line, different industry parts need to be quickly
differentiated and sorted for further process. Parts can be of different colors
and shapes. It is tedious for humans to differentiate and sort these objects in
appropriate categories. Automating this process would save more time and cost.
In the automation process, choosing an appropriate model to detect and classify
different objects based on specific features is more challenging. In this
paper, three different neural network models are compared to the object sorting
system. They are namely CNN, Fast R-CNN, and Faster R-CNN. These models are
tested, and their performance is analyzed. Moreover, for the object sorting
system, an Arduino-controlled 5 DoF (degree of freedom) robot arm is programmed
to grab and drop symmetrical objects to the targeted zone. Objects are
categorized into classes based on color, defective and non-defective objects.

5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex