  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.
