Deep Learning-Based Object Detection for Unmanned Aerial Systems (UASs)-Based Inspections of Construction Stormwater Practices

Thumbnail Image
Date
2021-04-17
Authors
Kazaz, Billur
Poddar, Subhadipto
Arabi, Saeed
Perez, Michael A.
Whitman, J. Blake
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Authors
Person
Research Projects
Organizational Units
Journal Issue
Is Version Of
Versions
Series
Department
Civil, Construction and Environmental EngineeringInstitute for Transportation
Abstract
Construction activities typically create large amounts of ground disturbance, which can lead to increased rates of soil erosion. Construction stormwater practices are used on active jobsites to protect downstream waterbodies from offsite sediment transport. Federal and state regulations require routine pollution prevention inspections to ensure that temporary stormwater practices are in place and performing as intended. This study addresses the existing challenges and limitations in the construction stormwater inspections and presents a unique approach for performing unmanned aerial system (UAS)-based inspections. Deep learning-based object detection principles were applied to identify and locate practices installed on active construction sites. The system integrates a post-processing stage by clustering results. The developed framework consists of data preparation with aerial inspections, model training, validation of the model, and testing for accuracy. The developed model was created from 800 aerial images and was used to detect four different types of construction stormwater practices at 100% accuracy on the Mean Average Precision (MAP) with minimal false positive detections. Results indicate that object detection could be implemented on UAS-acquired imagery as a novel approach to construction stormwater inspections and provide accurate results for site plan comparisons by rapidly detecting the quantity and location of field-installed stormwater practices.
Comments
This article is published as Kazaz, Billur, Subhadipto Poddar, Saeed Arabi, Michael A. Perez, Anuj Sharma, and J. Blake Whitman. "Deep Learning-Based Object Detection for Unmanned Aerial Systems (UASs)-Based Inspections of Construction Stormwater Practices." Sensors 21, no. 8 (2021): 2834. DOI: 10.3390/s21082834. Copyright 2021 by the authors. Attribution 4.0 International (CC BY 4.0). Posted with permission.
Description
Keywords
Citation
DOI
Copyright
Collections