In order to realize the automatic recognition and classification of cracks with different depths,in this study,several deep convolutional neural networks including AlexNet,ResNet,and DenseNet were employed to identify...In order to realize the automatic recognition and classification of cracks with different depths,in this study,several deep convolutional neural networks including AlexNet,ResNet,and DenseNet were employed to identify and classify cracks at different depths and in various materials.An analysis process for the automatic classification of crack damage was presented.The image dataset used for model training was obtained from scanning experiments on aluminum and titanium alloy plates using an ultrasonic phased-array flaw detector.All models were trained and validated with the dataset;the proposed models were compared using classification precision and loss values.The results show that the automatic recognition and classification of crack depth can be realized by using the deep learning algorithm to analyze the ultrasonic phased array images,and the classification precision of DenseNet is the highest.The problem that ultrasonic damage identification relies on manual experience is solved.展开更多
Identifying cracks from the spread image of a borehole wall is one of the most common usages of borehole imaging method. The manual identification of cracks is time-consuming and can be easily influenced by objective ...Identifying cracks from the spread image of a borehole wall is one of the most common usages of borehole imaging method. The manual identification of cracks is time-consuming and can be easily influenced by objective judgment. In this study, firstly, the image translation from RGB color model to HSV color model is done to highlight the structural plane region, which is closer to the color recognition of human sight; secondly, the Saturation component is filtered for further processing and a twice segmentation method is proposed to improve the accuracy of automatic identification. The primary segmentation is based on the statistics of saturation over a longer borehole section and can give a rough estimation of a crack. Then, the pixels are shifted in the reverse direction to the sine curve estimated and make the centerline of the crack flat. Based on the shifted image, the secondary segmentation is done with a small rectangle region that takes the baseline of the roughly estimated crack as its centerline. The result of the secondary segmentation can give a correction to the first estimation. Through verifying this method with actual borehole image data, the result has shown that this method can identify cracks automatically under very complicated geological conditions.展开更多
This paper proposes a detection method based on an improved Mask Region-based Convolutional Neural Network(Mask R-CNN)model for crack recognition in shallow-buried compressed air energy storage(CAES)cavern linings,ena...This paper proposes a detection method based on an improved Mask Region-based Convolutional Neural Network(Mask R-CNN)model for crack recognition in shallow-buried compressed air energy storage(CAES)cavern linings,enabling a comprehensive safety assessment of gas storage caverns.Flexible concrete samples are prepared to simulate the crack characteristics of the sealing lining,providing data support for the recognition module.The Convolutional Block Attention Module is introduced into the ResNet-50 backbone to adaptively adjust feature map weights and enhance feature extraction.Additionally,the mask segmentation loss function is optimized by combining Binary Cross-Entropy loss and Dice loss to improve crack region recognition.Experimental results show that the improved Mask R-CNN model achieves a mean average precision of 89.3%,a 17.2%improvement over the original model,and an intersection over union of 88.41%.Compared to RCNN,Faster R-CNN,YOLOv5,and SSD,the improved model shows superior performance with higher average precision(AP)50:95,AP50,and AP75 values in crack recognition tasks.The proposed method effectively identifies cracks in the flexible concrete sealing lining of shallow-buried CAES caverns,contributing significantly to the prevention of gas storage leaks and providing a valuable approach for the comprehensive safety assessment of CAES gas storage caverns.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52222504 and 52241502)the Natural Science Talents Foundation of Shaanxi Province(No.2021JC-04).
文摘In order to realize the automatic recognition and classification of cracks with different depths,in this study,several deep convolutional neural networks including AlexNet,ResNet,and DenseNet were employed to identify and classify cracks at different depths and in various materials.An analysis process for the automatic classification of crack damage was presented.The image dataset used for model training was obtained from scanning experiments on aluminum and titanium alloy plates using an ultrasonic phased-array flaw detector.All models were trained and validated with the dataset;the proposed models were compared using classification precision and loss values.The results show that the automatic recognition and classification of crack depth can be realized by using the deep learning algorithm to analyze the ultrasonic phased array images,and the classification precision of DenseNet is the highest.The problem that ultrasonic damage identification relies on manual experience is solved.
基金the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin,China Institute of Water Resources and Hydropower Research(No.IWHR-SKL-201304)the Youth Fund of State Key Laboratory of Ocean Engineering(No.GKZD010059-25)
文摘Identifying cracks from the spread image of a borehole wall is one of the most common usages of borehole imaging method. The manual identification of cracks is time-consuming and can be easily influenced by objective judgment. In this study, firstly, the image translation from RGB color model to HSV color model is done to highlight the structural plane region, which is closer to the color recognition of human sight; secondly, the Saturation component is filtered for further processing and a twice segmentation method is proposed to improve the accuracy of automatic identification. The primary segmentation is based on the statistics of saturation over a longer borehole section and can give a rough estimation of a crack. Then, the pixels are shifted in the reverse direction to the sine curve estimated and make the centerline of the crack flat. Based on the shifted image, the secondary segmentation is done with a small rectangle region that takes the baseline of the roughly estimated crack as its centerline. The result of the secondary segmentation can give a correction to the first estimation. Through verifying this method with actual borehole image data, the result has shown that this method can identify cracks automatically under very complicated geological conditions.
基金the financial supports of the National Natural Science Foundation of China Youth Science Foundation Project(52204152,52204111,52204153)the Postdoctoral Innovation Talent Support Program(BX2020275)the Postdoctoral Science Foundation(2020M683521).
文摘This paper proposes a detection method based on an improved Mask Region-based Convolutional Neural Network(Mask R-CNN)model for crack recognition in shallow-buried compressed air energy storage(CAES)cavern linings,enabling a comprehensive safety assessment of gas storage caverns.Flexible concrete samples are prepared to simulate the crack characteristics of the sealing lining,providing data support for the recognition module.The Convolutional Block Attention Module is introduced into the ResNet-50 backbone to adaptively adjust feature map weights and enhance feature extraction.Additionally,the mask segmentation loss function is optimized by combining Binary Cross-Entropy loss and Dice loss to improve crack region recognition.Experimental results show that the improved Mask R-CNN model achieves a mean average precision of 89.3%,a 17.2%improvement over the original model,and an intersection over union of 88.41%.Compared to RCNN,Faster R-CNN,YOLOv5,and SSD,the improved model shows superior performance with higher average precision(AP)50:95,AP50,and AP75 values in crack recognition tasks.The proposed method effectively identifies cracks in the flexible concrete sealing lining of shallow-buried CAES caverns,contributing significantly to the prevention of gas storage leaks and providing a valuable approach for the comprehensive safety assessment of CAES gas storage caverns.
