A deep learning based computer vision solution for construction vehicle detection
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. Disciplines Construction Engineering and Management 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. This article is available at Iowa State University Digital Repository: https:/lib.dr.iastate.edu/ccee_pubs/246
Comput. Aided Civ. Infrastructure Eng.