Fault Detection Method Based on Improved Faster R-CNN: Take ResNet-50 as an Example
In view of the low accuracy of existing tomographic detection methods, in order to improve the accuracy of tomographic detection, a tomographic detection method based on residual network and Faster R-CNN is proposed. First, input the image into the ResNet-50 feature extraction network to obtain the...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2022/7812410 |
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| _version_ | 1832545919843696640 |
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| author | Xie Renjun Yuan Junliang Wu Yi Shu Mengcheng |
| author_facet | Xie Renjun Yuan Junliang Wu Yi Shu Mengcheng |
| author_sort | Xie Renjun |
| collection | DOAJ |
| description | In view of the low accuracy of existing tomographic detection methods, in order to improve the accuracy of tomographic detection, a tomographic detection method based on residual network and Faster R-CNN is proposed. First, input the image into the ResNet-50 feature extraction network to obtain the corresponding feature map, then use the RPN structure to generate the candidate frame, and project the candidate frame generated by the RPN to the feature map to obtain the corresponding feature matrix, and finally, through the ROI pooling layer, each of the feature matrix is scaled to a fixed-size feature map, and then the feature map is flattened through a series of fully connected layers to obtain the prediction result. ResNet-50 mainly solves the problem of network degradation and overfitting caused by deepening of the network layer when extracting the deep features of faults. Faster R-CNN realizes end-to-end training, combines the advantages of ResNet-50 and Faster R-CNN, and has a precise positioning efficiency. The accuracy of detecting faults reaches 90%. The data enhancement is further optimized, the generalization ability of the network is improved, the detection results of the network are optimized, and the accuracy of fault detection is effectively improved, and the feasibility of the method is verified by actual seismic data. |
| format | Article |
| id | doaj-art-5b7d817b1661488fac0cbd6859d4ca5b |
| institution | Kabale University |
| issn | 1468-8123 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-5b7d817b1661488fac0cbd6859d4ca5b2025-02-03T07:24:17ZengWileyGeofluids1468-81232022-01-01202210.1155/2022/7812410Fault Detection Method Based on Improved Faster R-CNN: Take ResNet-50 as an ExampleXie Renjun0Yuan Junliang1Wu Yi2Shu Mengcheng3CNOOC Research Institute Co.CNOOC Research Institute Co.CNOOC Research Institute Co.China University of PetroleumIn view of the low accuracy of existing tomographic detection methods, in order to improve the accuracy of tomographic detection, a tomographic detection method based on residual network and Faster R-CNN is proposed. First, input the image into the ResNet-50 feature extraction network to obtain the corresponding feature map, then use the RPN structure to generate the candidate frame, and project the candidate frame generated by the RPN to the feature map to obtain the corresponding feature matrix, and finally, through the ROI pooling layer, each of the feature matrix is scaled to a fixed-size feature map, and then the feature map is flattened through a series of fully connected layers to obtain the prediction result. ResNet-50 mainly solves the problem of network degradation and overfitting caused by deepening of the network layer when extracting the deep features of faults. Faster R-CNN realizes end-to-end training, combines the advantages of ResNet-50 and Faster R-CNN, and has a precise positioning efficiency. The accuracy of detecting faults reaches 90%. The data enhancement is further optimized, the generalization ability of the network is improved, the detection results of the network are optimized, and the accuracy of fault detection is effectively improved, and the feasibility of the method is verified by actual seismic data.http://dx.doi.org/10.1155/2022/7812410 |
| spellingShingle | Xie Renjun Yuan Junliang Wu Yi Shu Mengcheng Fault Detection Method Based on Improved Faster R-CNN: Take ResNet-50 as an Example Geofluids |
| title | Fault Detection Method Based on Improved Faster R-CNN: Take ResNet-50 as an Example |
| title_full | Fault Detection Method Based on Improved Faster R-CNN: Take ResNet-50 as an Example |
| title_fullStr | Fault Detection Method Based on Improved Faster R-CNN: Take ResNet-50 as an Example |
| title_full_unstemmed | Fault Detection Method Based on Improved Faster R-CNN: Take ResNet-50 as an Example |
| title_short | Fault Detection Method Based on Improved Faster R-CNN: Take ResNet-50 as an Example |
| title_sort | fault detection method based on improved faster r cnn take resnet 50 as an example |
| url | http://dx.doi.org/10.1155/2022/7812410 |
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