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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Computer-aided civil and infrastructure engineering Ročník 35; číslo 7; s. 753 - 767
Hlavní autoři: Arabi, Saeed, Haghighat, Arya, Sharma, Anuj
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken Wiley Subscription Services, Inc 01.07.2020
Témata:
Computer vision
Decision making
Deep learning
Economic analysis
Economic models
Electronic devices
Embedded systems
Image detection
Object recognition
Optimization
ISSN:1093-9687, 1467-8667
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1093-9687
1467-8667
DOI:10.1111/mice.12530