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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computer-aided civil and infrastructure engineering Jg. 35; H. 7; S. 753 - 767
Hauptverfasser: Arabi, Saeed, Haghighat, Arya, Sharma, Anuj
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Wiley Subscription Services, Inc 01.07.2020
Schlagworte:
Computer vision
Decision making
Deep learning
Economic analysis
Economic models
Electronic devices
Embedded systems
Image detection
Object recognition
Optimization
ISSN:1093-9687, 1467-8667
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1093-9687
1467-8667
DOI:10.1111/mice.12530