Campus Units

Civil, Construction and Environmental Engineering, Institute for Transportation

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

7-2020

Journal or Book Title

Computer-Aided Civil and Infrastructure Engineering

Volume

35

Issue

7

First Page

753

Last Page

767

DOI

10.1111/mice.12530

Abstract

This paper aims at providing researchers and engineering professionals from the first step of solution development to the last step of solution deployment with a practical and comprehensive deep‐learning‐based solution for detecting construction vehicles. This paper places particular focus on the often‐ignored last step of deployment. Our first phase of solution development involved data preparation, model selection, model training, and model validation. Given the necessarily small‐scale nature of construction vehicle image datasets, we propose as detection model an improved version of the single shot detector MobileNet, which is suitable for embedded devices. Our study's second phase comprised model optimization, application‐specific embedded system selection, economic analysis, and field implementation. Several embedded devices were proposed and compared. Results including a consistent above 90% mean average precision confirm the superior real‐time performance of our proposed solutions. Finally, the practical field implementation of our proposed solutions was investigated. This study validates the practicality of deep‐learning‐based object detection solutions for construction scenarios. Moreover, the detailed information provided by the current study can be employed for several purposes such as safety monitoring, productivity assessments, and managerial decision making.

Research Focus Area

Transportation Engineering

Comments

This is the pre-peer reviewed version of the following article: Arabi, Saeed, Arya Haghighat, and Anuj Sharma. "A deep‐learning‐based computer vision solution for construction vehicle detection." Computer‐Aided Civil and Infrastructure Engineering 35, no. 7 (2020): 753-767, which has been published in final form at DOI: 10.1111/mice.12530. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Posted with permission.

Copyright Owner

Computer‐Aided Civil and Infrastructure Engineering

Language

en

File Format

application/pdf

Published Version

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