DISTRESS DETECTION OF ROAD IMAGES USING DEEP LEARNING

1 online resource (83 pages) : PDF

2019

University of North Carolina at Charlotte

The NCDOT road survey involves capturing images of highway and later processing to determine an overall pavement performance. Terabytes of data is captured even over a small highway section, therefore, there is a need to find better solutions to process these images. This study aims to label and detect distresses on the collected images using deep learning. Previous studies have manually labeled images to develop a labeled dataset, however, there is no documented record of this process. Therefore, this research enumerates the steps required to develop a labeled dataset from the raw images and utilize these images to develop several Mask-RCNN models. In addition, previous studies have utilized deep learning models to classify road survey images, however, this study utilizes Mask-RCNN to not only detect the different distresses present on an image but also to classify its type. This study identified Adobe Photoshop as the ideal labelling software for survey images. Mask-RCNN models were developed using python with Tensorflow, Keras and opencv2 libraries. Several parameters were changed and a total of five models were tested on training and test road survey images. The results were promising, and further studies need to develop more accurate models for conclusive results. The findings of this study can be applied by other transportation agencies on other road survey images.

masters theses

Civil engineering

M.S.

Construction & Facilities Mgmt

Chen, Don

Smithwick, Jake
Sherlock, Barry

Thesis (M.S.)--University of North Carolina at Charlotte, 2019.

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Goud_uncc_0694N_12118

http://hdl.handle.net/20.500.13093/etd:1967