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Pixel‐level Thin Crack Detection on Road Surface Using Convolutional Neural Network for Severely Imbalanced Data by Thitirat Siriborvornratanakul is a scholarly article available to read on EtoBox.

What is Pixel‐level Thin Crack Detection on Road Surface Using Convolutional Neural Network for Severely Imbalanced Data about?

Because roads are the major backbone of the transportation network, research about crack detection on road surfaces has been popular in computer and infrastructure engineering. When training a convolutional neural network (CNN) for pixel-level road crack detection, three common challenges include (1) the data are severely imbalanced, (2) crack pixels can be easily confused with normal road texture and other visual noises, and (3) there are many unexplainable characteristics regarding the CNN itself. When it comes to very fine and thin cracks, these challenges are exaggerated and a new challenge is introduced, as there can be a discrepancy between the actual width and the annotated width of a crack. To tackle all these challenges of thin crack detection, this paper proposes a new variant of CNN named ThinCrack U-Net, designed to provide thin results upon pixel-level crack detection on road surfaces. The main contribution is to demystify how pixel-level thin crack detection results are affected by different loss functions as well as various combinations of the U-Net components. The experimental results show that ThinCrack U-Net yields a significant performance boost in CrackTree260,

Author

Thitirat Siriborvornratanakul

Publisher

Wiley

Published

2023

Language

EN