3 research outputs found
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An improved generative adversarial network with modified loss function for crack detection in electromagnetic nondestructive testing
In this paper, an improved generative adversarial network (GAN) is proposed for the crack detection problem in electromagnetic nondestructive testing (NDT). To enhance the contrast ratio of the generated image, two additional regulation terms are introduced in the loss function of the underlying GAN. By applying an appropriate threshold to the segmentation of the generated image, the real crack areas and the fake crack areas (which are affected by the noises) are accurately distinguished. Experiments are carried out to show the superiority of the improved GAN over the original one on crack detection tasks, where a real-world NDT dataset is exploited that consists of magnetic optical images obtained using the electromagnetic NDT technique.Institutional Fund Projects; Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia; National Natural Science Foundation of China; China Postdoctoral Science Foundation; Royal Society; Alexander von Humboldt Foundatio
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A New GAN-Based Approach to Data Augmentation and Image Segmentation for Crack Detection in Thermal Imaging Tests
© The Author(s) 2021. As a popular nondestructive testing (NDT) technique, thermal imaging test demonstrates competitive performance in crack detection, especially for detecting subsurface cracks. In thermal imaging test, the temperature of the crack area is higher than that of the non-crack area during the NDT process. By extracting the features of the thermal image sequences, the temperature curve of each spatial point is employed for crack detection. Nevertheless, the quality of thermal images is influenced by the noises due to the complex thermal environment in NDT. In this paper, a modified generative adversarial network (GAN) is employed to improve the image segmentation performance. To improve the feature extraction ability and alleviate the influence of noises, a penalty term is put forward in the loss function of the conventional GAN. A data preprocessing method is developed where the principle component analysis algorithm is adopted for feature extraction. The data argumentation technique is utilized to guarantee the quantity of the training samples. To validate its effectiveness in thermal imaging NDT, the modified GAN is applied to detect the cracks on the eddy current pulsed thermography NDT dataset.Institutional Fund Projects under grant no. (IFPIP-220-135-1442); Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia; National Natural Science Foundation of China under Grants 61873148, 61933007 and 61903065; China Postdoctoral Science Foundation under Grant 2018M643441; Royal Society of the UK; Alexander von Humboldt Foundation of Germany
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Empower parameterized generative adversarial networks using a novel particle swarm optimizer: algorithms and applications
Copyright © The Author(s) 2021. In this paper, a novel parameterized generative adversarial network (GAN) is proposed where the parameters are introduced to enhance the performance of image segmentation. The developed algorithm is applied to the image-based crack detection problem on the thermal data obtained through the non-destructive testing process. A new regularization term, which contains three tunable hyperparameters, embedded into the objective function of the GAN in order to improve the contrast ratio of certain areas of the image so as to benefit the crack detection process. To automate the selection of the optimal hyperparameters of the GAN, a new particle swarm optimization (PSO) algorithm is put forward where a neighborhood-based velocity updating strategy is developed for the purpose of thoroughly exploring the problem space. The proposed PSO-based GAN algorithm is shown to 1) work well in detecting cracks on the thermal data generated by the eddy current pulsed thermography technique; and 2) outperforms other conventional GAN algorithms.This research work was funded by Institutional Fund Projects under grant no. (IFPIP-221-135-1442). Therefore, the authors gratefully acknowledge technical and fnancial support from the Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia. This work was also supported in part by the National Natural Science Foundation of China under Grants 61873148, 61933007 and 61903065, the China Postdoctoral Science Foundation under Grant 2018M643441, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany