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Indexed by:会议论文

Date of Publication:2017-09-12

Journal:11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017

Included Journals:EI、Scopus

Volume:1

Page Number:978-984

Abstract:Crack is one of important features in gradually degeneration process for structures, and the cracks of structure can to some extent reflect the health status of the structure. Moreover, convolutional neural networks (CNNs) provide an ideal route to detect cracks utilizing the explosion of Internet and the improvement of computer hardware. In this paper, a method for crack detection based on CNNs is proposed. Firstly, to provide datasets for CNN model training, a large number of crack images ware collected, cut, and labeled. Secondly, a CNN model for crack detection was established through the fine-tuning AlexNet model. Thirdly, the CNN model was trained by the crack images datasets. The trained CNN model is used as a classifier and the crack detection was implemented using the CNN classifier and a sliding window technique. Finally, a crowdsourcing to collect crack images and detect crack is proposed. The objectives of this work are to set up a crack images database for more researchers and obtain a more and more accurate and trained classifier for crack detection via CNNs, and mobilizing the public to collect crack images and detect crack using the trained CNN classifier through the crowdsourcing.