Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through Deep-Learning- Based CrackNet-V
Autor: | Joshua Q. Li, Kelvin C. P. Wang, Baoxian Li, Yue Fei, Yang Liu, Allen Zhang, Guangwei Yang, Cheng Chen |
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Rok vydání: | 2020 |
Předmět: |
050210 logistics & transportation
Pixel business.industry Computer science Mechanical Engineering Computation Deep learning 05 social sciences Hyperbolic function Activation function Supervised learning Feature extraction Computer Science Applications Kernel (image processing) 0502 economics and business Automotive Engineering Artificial intelligence business Algorithm |
Zdroj: | IEEE Transactions on Intelligent Transportation Systems. 21:273-284 |
ISSN: | 1558-0016 1524-9050 |
Popis: | A few recent developments have demonstrated that deep-learning-based solutions can outperform traditional algorithms for automated pavement crack detection. In this paper, an efficient deep network called CrackNet-V is proposed for automated pixel-level crack detection on 3D asphalt pavement images. Compared with the original CrackNet, CrackNet-V has a deeper architecture but fewer parameters, resulting in improved accuracy and computation efficiency. Inspired by CrackNet, CrackNet-V uses invariant spatial size through all layers such that supervised learning can be conducted at pixel level. Following the VGG network, CrackNet-V uses $3\times 3$ size of filters for the first six convolutional layers and stacks several $3\times 3$ convolutional layers together for deep abstraction, resulting in reduced number of parameters and efficient feature extraction. CrackNet-V has 64113 parameters and consists of ten layers, including one pre-process layer, eight convolutional layers, and one output layer. A new activation function leaky rectified tanh is proposed in this paper for higher accuracy in detecting shallow cracks. The training of CrackNet-V was completed after 3000 iterations, which took only one day on a GeForce GTX 1080Ti device. According to the experimental results on 500 testing images, CrackNet-V achieves a high performance with a Precision of 84.31%, Recall of 90.12%, and an F-1 score of 87.12%. It is shown that CrackNet-V yields better overall performance particularly in detecting fine cracks compared with CrackNet. The efficiency of CrackNet-V further reveals the advantages of deep learning techniques for automated pixel-level pavement crack detection. |
Databáze: | OpenAIRE |
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