Pixel‐level crack delineation in images with convolutional feature fusion. Issue 1 (31st October 2018)

• Record Type: Journal Article
• Authors: Ni, FuTao; Zhang, Jian; Chen, ZhiQiang
• Is Part Of: Structural control and health monitoring. Volume 26:Issue 1(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-10-31
• Subjects: crack detection -- deep learning -- digital image -- features fusion -- transfer learning; Structural engineering -- Periodicals; Structural control (Engineering) -- Periodicals; Automatic data collection systems -- Periodicals; Detectors -- Periodicals; 624.17
• DOI: 10.1002/stc.2286
• Languages: English
• ISSNs: 1545-2255

Abstract

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.