Abstract

Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task, however, remains challenging due to the high variations in road objects and pavement types, variety of lighting condition, low contrast, and background noises in pavement images. In this dissertation, we propose novel deep learning algorithms for image-based road condition assessment to tackle current challenges in detection, classification and segmentation of pavement images. Motivated by the need for classifying a wide range of objects in road monitoring, this dissertation introduces a Multi-Scale Convolution Neural Network (MCNN) for multi-class classification of pavement images. MCNN improves the classification performance by encoding contextual information through multi-scale input tiles. Then, an Attention-Based Multi-Scale CNN (A+MCNN) is proposed to further improve the classification results through a novel mid-fusion strategy for combining multi-scale features extracted from multi-scale input tiles. An attention module is designed as an adaptive fusion strategy to generate importance scores and integrate multi-scale features based on how informative they are to the classification task. Finally, Dual Attention CNN (DACNN) is introduced to improve the performance of multi-class classification using both intensity and range images collected with 3D laser imaging devices. DACNN integrates information in intensity and range images to enhance distinct features improving the objects classification in noisy images under various illumination conditions. The standard road condition assessment includes determining not only the type of defects but also the severity of detects. In this regard, a pavement crack segmentation algorithm, CrackSegmenter, is proposed to detect crack at pixel level. The CrackSegmenter leverages residual blocks, attention blocks, Atrous Spatial Pyramid Pooling (ASSP), and squeeze and excitation blocks to improve segmentation performance in pavement crack images.

Notes

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Graduation Date

2022

Semester

Summer

Advisor

Yun, Hae-Bum

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Civil, Environmental and Construction Engineering

Degree Program

Civil Engineering

Identifier

CFE0009167; DP0026763

URL

https://purls.library.ucf.edu/go/DP0026763

Language

English

Release Date

August 2022

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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