Runet: Convolutional Networks for Crack Detection
| Autor: | Lin Hu, Fuping Yang |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Journal of Physics: Conference Series. 2171:012052 |
| ISSN: | 1742-6596 1742-6588 |
| DOI: | 10.1088/1742-6596/2171/1/012052 |
| Popis: | In daily life, crack is a common phenomenon. Such as road cracks, stone cracks. These cracks are usually not easy to be quickly and intuitively identified, because these cracks are characterized by poor continuity and low contrast. Among the existing crack detection methods based on deep learning, the model is too large to be directly used in life. We design an end-to-end neural network, its name is Runet, which greatly reduces the parameters of the model by depthwise separable convolution, increases the receptive field of the network by hole convolution. At the same time, we introduce the trainingtime and inferencetime architecture to increase the running speed and precision. |
| Databáze: | OpenAIRE |
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