Expansive soil crack image recognition method based on convolutional neural network
The invention discloses an expansive soil crack image recognition method based on a convolutional neural network, and the method comprises the following steps: 1, collecting an expansive soil sample, and carrying out the CT scanning of the expansive soil sample, and obtaining a CT image; step 2, pro...
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Format | Patent |
Language | Chinese English |
Published |
25.10.2022
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Abstract | The invention discloses an expansive soil crack image recognition method based on a convolutional neural network, and the method comprises the following steps: 1, collecting an expansive soil sample, and carrying out the CT scanning of the expansive soil sample, and obtaining a CT image; step 2, processing the CT image obtained in the step 1 to obtain a binarized image; 3, labeling the binarized images, grouping the binarized images, and establishing a sample set 1 and a sample set 2; step 4, establishing a convolutional neural network model and training the convolutional neural network model by using the sample set 1; 5, performing accuracy evaluation on the convolutional neural network model trained in the step 4 by using the sample set 2; if the difference between the two is not more than 2%, the convolutional neural network model is successfully trained, otherwise, a random inactivation layer is added for re-training; and 6, identifying the expansive soil crack picture by using the trained convolutional n |
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AbstractList | The invention discloses an expansive soil crack image recognition method based on a convolutional neural network, and the method comprises the following steps: 1, collecting an expansive soil sample, and carrying out the CT scanning of the expansive soil sample, and obtaining a CT image; step 2, processing the CT image obtained in the step 1 to obtain a binarized image; 3, labeling the binarized images, grouping the binarized images, and establishing a sample set 1 and a sample set 2; step 4, establishing a convolutional neural network model and training the convolutional neural network model by using the sample set 1; 5, performing accuracy evaluation on the convolutional neural network model trained in the step 4 by using the sample set 2; if the difference between the two is not more than 2%, the convolutional neural network model is successfully trained, otherwise, a random inactivation layer is added for re-training; and 6, identifying the expansive soil crack picture by using the trained convolutional n |
Author | BAE YUN-SANG LING XIANCHANG LI XINYU TANG LIANG CHENG ZHIHE CONG SHENGYI MAO XIAOGANG CHEN HONGWEI ZHANG ZHONGYUAN ZHANG XIYANG |
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DocumentTitleAlternate | 一种基于卷积神经网络的膨胀土裂隙图像识别方法 |
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Snippet | The invention discloses an expansive soil crack image recognition method based on a convolutional neural network, and the method comprises the following steps:... |
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Title | Expansive soil crack image recognition method based on convolutional neural network |
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