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 of...
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| Vydané v: | Computer-aided civil and infrastructure engineering Ročník 35; číslo 7; s. 753 - 767 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Hoboken
Wiley Subscription Services, Inc
01.07.2020
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| Predmet: | |
| ISSN: | 1093-9687, 1467-8667 |
| On-line prístup: | Získať plný text |
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| 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. |
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| AbstractList | 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. |
| Author | Haghighat, Arya Sharma, Anuj Arabi, Saeed |
| Author_xml | – sequence: 1 givenname: Saeed surname: Arabi fullname: Arabi, Saeed organization: Iowa State University – sequence: 2 givenname: Arya surname: Haghighat fullname: Haghighat, Arya organization: Iowa State University – sequence: 3 givenname: Anuj surname: Sharma fullname: Sharma, Anuj email: anujs@iastate.edu organization: Iowa State University |
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| Title | A deep‐learning‐based computer vision solution for construction vehicle detection |
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