Coupling Deep Learning and UAV for Infrastructure Condition Assessment Automation

Wu, Weidong; Qurishee, Murad Al; Owino, Joseph; Fomunung, Ignatius; Onyango, Mbakisya; Atolagbe, Babatunde

doi:10.1109/isc2.2018.8656971

Cited by 46 publications

(28 citation statements)

References 11 publications

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“…Structural health monitoring [36] One of the major challenges civil engineering community face in applying deep learning in bridge inspection is shortage of an image dataset that has good representation of all bridge components to be inspected [38].…”

Section: The Usdot's Intelligent Transportation Systems (Its) Its Strmentioning

confidence: 99%

Creating a Dataset to Boost Civil Engineering Deep Learning Research and Application

Qurishee¹,

Wu²,

Atolagbe³

et al. 2020

ENG

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With cutting edge deep learning breakthrough, numerous innovations in many fields including civil engineering are stimulated. However, a fundamental issue that civil engineering research community currently facing is lack of a publicly available, free, quality-controlled and human-annotated large dataset that supports and drives civil engineering deep learning research and applications on such as intelligent transportation including connected vehicle, structural health monitoring, and bridge inspection. This paper is a general discussion about demanding needs and construction of a long-anticipated dataset for researchers and engineers in civil engineering and beyond for providing critical training, testing and benchmarking data. The establishment of such a free dataset will remove a major hurdle and boost deep learning research in civil engineering and we hope this work will urge researchers, engineers, government agencies and even computer scientists to work together to start building such datasets. A framework has been developed for the proposed database. Also, some pilot study databases were developed for concrete crack detection, pavement crack detection using normal and infrared thermography, as well as pedestrian and bicyclist detection. A convolution neural network model called Faster RCNN was deployed to check the detection accuracy and a 98% detection accuracy of the proposed datasets was obtained.

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Section: The Usdot's Intelligent Transportation Systems (Its) Its Strmentioning

confidence: 99%

Creating a Dataset to Boost Civil Engineering Deep Learning Research and Application

Qurishee¹,

Wu²,

Atolagbe³

et al. 2020

ENG

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“…With the drone a FLIR 6.8 mm f/1.3 thermal imaging camera were incorporated to capture the thermal images, damages inside Journal of Transportation Technologies Table 1. Comparison of image processing models [1].…”

Section: Data Acquisitionmentioning

confidence: 99%

“…However, modern challenges in infrastructure development transcend merely building of roads, bridges, and other social facilities, but rather exploring means to mitigate the deterioration. Certainly, the rate of deterioration of US infrastructure, for example, has been a subject of concern amongst politicians, engineers and the public at large [1]. According to the ASCE 2017 report card, the US infrastructure received a cumulative grade of D+ (i.e.…”

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confidence: 99%

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Bridge Girder Crack Assessment Using Faster RCNN Inception V2 and Infrared Thermography

Qurishee¹,

Wu²,

Atolagbe³

et al. 2020

JTTs

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Manual inspections of infrastructures such as highway bridge, pavement, dam, and multistoried garage ceiling are time consuming, sometimes can be life threatening, and costly. An automated computerized system can reduce time, faulty inspection, and cost of inspection. In this study, we developed a computer model using deep learning Convolution Neural Network (CNN), which can be used to automatically detect the crack and non-crack type structure. The goal of this research is to allow application of state-of-the-art deep neural network and Unmanned Aerial Vehicle (UAV) technologies for highway bridge girder inspection. As a pilot study of implementing deep learning in Bridge Girder, we study the recognition, length, and location of crack in the structure of the UTC campus old garage concrete ceiling slab. A total of 2086 images of crack and non-crack were taken from UTC Old Library parking garage ceiling using handheld mobile phone and drone. After training the model shows 98% accuracy with crack and non-crack types of structures.

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“…Hence the conventional method of assessment is not more useful in the future by using visible and near-infrared light individually. The UAVs are the best assessment approaches through which the scanning of crops are very easy to identify the diseases and agronomy measures can be taken for proper production (Wu et al, 2018).…”

Section: Uav In Health Assessment Of Cropsmentioning

confidence: 99%

Applications of unmanned aerial vehicles: a review

Nawaz¹,

Ali²,

Massan³

2019

3C Tecnología

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Coupling Deep Learning and UAV for Infrastructure Condition Assessment Automation

Cited by 46 publications

References 11 publications

Creating a Dataset to Boost Civil Engineering Deep Learning Research and Application

Creating a Dataset to Boost Civil Engineering Deep Learning Research and Application

Bridge Girder Crack Assessment Using Faster RCNN Inception V2 and Infrared Thermography

Applications of unmanned aerial vehicles: a review

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