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Pixel‐level crack delineation in images with convolutional feature fusion
Author(s) -
Ni FuTao,
Zhang Jian,
Chen ZhiQiang
Publication year - 2019
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2286
Subject(s) - convolutional neural network , artificial intelligence , feature (linguistics) , computer science , pixel , segmentation , computer vision , image (mathematics) , deep learning , pattern recognition (psychology) , philosophy , linguistics
Summary Cracks in civil structures are important signs of structural degradation and may even indicate the inception of catastrophic failure. Image‐based crack detection has been attempted in research communities that bear the potential of replacing human‐based inspection. Among many methodologies, deep learning‐based cracks detection is actively explored in recent years. However, how to automatically extract cracks quickly and accurately at a pixel level, that is, crack delineation (including both detection and segmentation), is a challenging issue. This article proposes a convolutional neural network‐based framework that automates this task through convolutional feature fusion and pixel‐level classification. The resulting network architecture with an empirically optimal fusion strategy, termed the crack delineation network, is trained and tested based on a concrete crack image database. The results show that the proposed framework can delineate cracks accurately and rapidly in images towards a fully autonomous machine vision approach to structural crack detection.