· 2016
Road crack detection using deep convolutional neural network
- 1,468citations
- 2016year
Short summary
A new deep learning method using a supervised convolutional neural network effectively detects pavement cracks, outperforming traditional hand-crafted feature methods.
AI-generated from the title and abstract; the full text is not read.
Key points
- Developed a deep convolutional neural network for automatic road crack detection.
- Trained the network to classify image patches for crack identification.
- Achieved superior performance over hand-crafted feature methods.
- Evaluated on a dataset of 500 images captured by a smartphone.
AI-generated from the title and abstract; the full text is not read.
Abstract
Automatic detection of pavement cracks is an important task in transportation maintenance for driving safety assurance. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the background, e.g., the low contrast with surrounding pavement and possible shadows with similar intensity. Inspired by recent success on applying deep learning to computer vision and medical problems, a deep-learning based method for crack detection is proposed in this paper. A supervised deep convolutional neural network is trained to classify each image patch in the collected images. Quantitative evaluation conducted on a data set of 500 images of size 3264 χ 2448, collected by a low-cost smart phone, demonstrates that the learned deep features with the proposed deep learning framework provide superior crack detection performance when compared with features extracted with existing hand-craft methods.
The authors' abstract, as published at the source., 2016 · DOI ↗
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Field: Civil and Structural Engineering
Civil and Structural EngineeringEngineering