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Your search keyword '"Ivashov, Sergey"' showing total 105 results
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105 results on '"Ivashov, Sergey"'

2. A deep learning framework based on improved self-supervised learning for ground-penetrating radar tunnel lining inspection.

Author

Jian Huang, Xi Yang, Feng Zhou, Xiaofeng Li, Bin Zhou, Song Lu, Ivashov, Sergey, Giannakis, Iraklis, Kong, Fannian, and Slob, Evert

Abstract

It is not practical to obtain a large number of labeled data to train a supervised learning network in tunnel lining nondestructive testing with groundpenetrating radar (GPR). To decrease the dependence of supervised learning on the number of labeled data, an improved self-supervised learning algorithm--self-attention dense contrastive learning (SA-DenseCL)--is proposed and incorporated with a mask region-convolution neural network (Mask R-CNN), which is trained by unlabeled and labeled GPR data. The proposed SA-DenseCL adds a self-attention-based relevant projection head to the DenseCL architecture of self-supervised learning, capturing the spatially continuing information between adjacent GPR traces. In the workflow, some unlabeled GPR images are used to pre-train the SA-DenseCL network for feature extraction and obtaining the backbone weights, which is superior to the conventional pre-training methods of supervised learning pre-trained by ImageNet images. The weights of the pre-trained backbone are then used to initialize the Mask R-CNN through transfer learning. Subsequently, a limited number of labeled GPR images are used to fine-tune the Mask R-CNN for automatically identifying the locations of the reinforcement bars and voids and estimating the secondary lining thickness. The experimental results show that the average precision reaches 96.70%, 81.04%, and 94.67% in identifying reinforcement bar locations, detecting void defects, and estimating secondary lining thickness, respectively, which outperform the conventional methods that use ImageNet-based supervised learning or GPR image-based DenseCL for initializing the Mask R-CNN backbone weights. It is observed that the improved self-supervised learning-based framework can improve the detection and estimation accuracy in GPR tunnel lining inspection. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Selection of Wavelet Transform and Neural Network Parameters for Classification of Breathing Patterns of Bio-radiolocation Signals

Author

Alekhin, Maksim, Anishchenko, Lesya, Tataraidze, Alexander, Ivashov, Sergey, Korostovtseva, Lyudmila, Sviryaev, Yurii, Bogomolov, Alexey, Junqueira Barbosa, Simone Diniz, editor, Chen, Phoebe, editor, Cuzzocrea, Alfredo, editor, Du, Xiaoyong, editor, Filipe, Joaquim, editor, Kara, Orhun, editor, Kotenko, Igor, editor, Sivalingam, Krishna M., editor, Ślęzak, Dominik, editor, Washio, Takashi, editor, Yang, Xiaokang, editor, Pham, Tuan D., editor, Ichikawa, Kazuhisha, editor, Oyama-Higa, Mayumi, editor, Coomans, Danny, editor, and Jiang, Xiaoyi, editor

Published
2014
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6. Bioradiolocation: Methods and Applications

Author

Anishchenko, Lesya, Alekhin, Maksim, Ivashov, Sergey, Ryzhii, Maxim, Junqueira Barbosa, Simone Diniz, editor, Chen, Phoebe, editor, Cuzzocrea, Alfredo, editor, Du, Xiaoyong, editor, Filipe, Joaquim, editor, Kara, Orhun, editor, Kotenko, Igor, editor, Sivalingam, Krishna M., editor, Ślęzak, Dominik, editor, Washio, Takashi, editor, Yang, Xiaokang, editor, Pham, Tuan D., editor, Ichikawa, Kazuhisha, editor, Oyama-Higa, Mayumi, editor, Coomans, Danny, editor, and Jiang, Xiaoyi, editor

Published
2014
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42. Development and Applications of the Holographic Subsurface RASCAN Radar

Author

Ivashov, Sergey I., Razevig, Vladimir V., Vasiliev, Igor A., Zhuravlev, Andrey V., Bechtel, Timothy D., and Capineri, Lorenzo

Subjects
holographic subsurface radar, humanitarian demining, impulse subsurface radar, lossy medium, non-destructive testing, optical analogy, security applications
Published
2010

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