Hierarchical Convolutional Neural Network With Feature Preservation and Autotuned Thresholding for Crack Detection

Drone imagery is increasingly used in automated inspection for infrastructure surface defects, especially in hazardous or unreachable environments. In machine vision, the key to crack detection rests with robust and accurate algorithms for image processing. To this end, this paper proposes a deep le...

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Veröffentlicht in:	IEEE access Jg. 9; S. 60201 - 60214
Hauptverfasser:	Zhu, Qiuchen, Dinh, Tran Hiep, Phung, Manh Duong, Ha, Quang Phuc
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial neural networks Bridge inspection Computational modeling Convolutional neural networks crack detection Datasets Deep learning Feature extraction Feature maps feature preserving Hazardous areas hierarchical convolutional neural network Image processing Inspection Iterative methods Kernel Machine learning Machine vision Monorails Neural networks Occultation Post-production processing Surface cracks Surface defects thresholding Unmanned aerial vehicles
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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