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• W4377971341 abstract "Due to the critical role of pavement crack detection for road maintenance and eventually ensuring safety, remarkable efforts have been devoted to this research area, and such a trend is further intensified for the coming unmanned vehicle era. However, such crack detection task still remains unexpectedly challenging in practice since the appearance of both cracks and the background are diverse and complex in real scenarios. In this work, we propose an automatic pavement crack detection method via synergizing low rank representation (LRR) and deep learning techniques. First, leveraging LRR which facilitates anomaly detection without making any specific assumption, we can easily discriminate most of the frames with cracks from the long sequence with a consistent pavement base, followed by a straightforward algorithm to localize the cracks. In order to achieve the intelligence of detecting cracks with different pavement basis under unconstrained imaging conditions, we resort to deep learning techniques and propose a deep convolutional neural network for crack detection leveraging on multi-level features and atrous spatial pyramid pooling (ASPP). We train this network based on the training data obtained in the previous stage in an end-to-end manner. Extensive experiments on a wide range of pavements demonstrate the high performance in terms of both accuracy and automaticity. Moreover, the dataset generated by us is much more extensive and challenging than public ones. We put it online at <uri xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>https://gaozhinuswhu.com</uri> to benefit the community." @default.
• W4377971341 created "2023-05-25" @default.
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• W4377971341 date "2023-10-01" @default.
• W4377971341 modified "2023-10-17" @default.
• W4377971341 title "Synergizing Low Rank Representation and Deep Learning for Automatic Pavement Crack Detection" @default.
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• W4377971341 doi "https://doi.org/10.1109/tits.2023.3275570" @default.
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