Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A deep learning based solution for construction equipment detection:
  from development to deployment   This paper aims at providing researchers and engineering professionals with a
practical and comprehensive deep learning based solution to detect construction
equipment from the very first step of its development to the last one which is
deployment. This paper focuses on the last step of deployment. The first phase
of solution development, involved data preparation, model selection, model
training, and model evaluation. The second phase of the study comprises of
model optimization, application specific embedded system selection, and
economic analysis. Several embedded systems were proposed and compared. The
review of the results confirms superior real-time performance of the solutions
with a consistent above 90% rate of accuracy. The current study validates the
practicality of deep learning based object detection solutions for construction
scenarios. Moreover, the detailed knowledge, presented in this study, can be
employed for several purposes such as, safety monitoring, productivity
assessments, and managerial decisions.

12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph