FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4362453650> ?p ?o ?g. }

• W4362453650 endingPage "116058" @default.
• W4362453650 startingPage "116058" @default.
• W4362453650 abstract "Crack detection is vital to maintain the structural safety of in-service bridges, which is an increasing demand in the industrial community. The deep learning-based crack detection is an emerging method that provides a novel way to deal with this problem. Although, the deep neural networks (DNN) can learn to detect cracks themselves, they require large numbers of crack images to learn the features of cracks in the real world. Besides, except for the crack forms, there always exist all kinds of noise motifs that will disturb the correct detection of crack regions. The lack of crack sample images has been an obstacle for the improvement of deep learning-based crack detection method. This paper proposes a generative adversarial network (GAN) based method to establish a synthesized crack image dataset with pixel-wise annotations, which provides a novel way aside from the traditional data augmentation method. The Deep Convolutional GAN (DCGAN) model was adopted for the generation of synthesized crack annotations while the Pixel2Pixel model was used to generate the corresponding synthesized crack images. The generated crack annotations and crack images during the training epochs were demonstrated to show how the GANs learn to generate the synthesized images. Moreover, comparative study was conducted to validate the performance of the synthesized crack image dataset for training the crack detection DNN. The results showed the DNN trained by the synthesized images can achieve 74.34% of the MeanIoU that was reached by the same DNN model trained with real images. As for the way of using the synthesized and real crack images for training a crack detection DNN, the way that use the synthesized crack images for pre-training and then use the real images for fine-tuning is better than the way that directly mix the synthesized and real crack images for training. This work provides reference for the GAN-based establishment of crack image dataset and the evaluation of the image quality for training crack detection DNNs." @default.
• W4362453650 created "2023-04-05" @default.
• W4362453650 creator A5004328892 @default.
• W4362453650 creator A5083516684 @default.
• W4362453650 creator A5089520262 @default.
• W4362453650 date "2023-06-01" @default.
• W4362453650 modified "2023-10-17" @default.
• W4362453650 title "Establishment and evaluation of conditional GAN-based image dataset for semantic segmentation of structural cracks" @default.
• W4362453650 cites W2020085751 @default.
• W4362453650 cites W2095359136 @default.
• W4362453650 cites W2757455114 @default.
• W4362453650 cites W2779497038 @default.
• W4362453650 cites W2807042118 @default.
• W4362453650 cites W2808943413 @default.
• W4362453650 cites W2887597701 @default.
• W4362453650 cites W2896613037 @default.
• W4362453650 cites W2899144041 @default.
• W4362453650 cites W2905163589 @default.
• W4362453650 cites W2908193436 @default.
• W4362453650 cites W2919816425 @default.
• W4362453650 cites W2922073063 @default.
• W4362453650 cites W2948461581 @default.
• W4362453650 cites W2954996726 @default.
• W4362453650 cites W2963073614 @default.
• W4362453650 cites W2963830453 @default.
• W4362453650 cites W2972460946 @default.
• W4362453650 cites W2978183057 @default.
• W4362453650 cites W3002057796 @default.
• W4362453650 cites W3013406096 @default.
• W4362453650 cites W3021470593 @default.
• W4362453650 cites W3022492425 @default.
• W4362453650 cites W3031761438 @default.
• W4362453650 cites W3034059856 @default.
• W4362453650 cites W3034109443 @default.
• W4362453650 cites W3034609614 @default.
• W4362453650 cites W3044580098 @default.
• W4362453650 cites W3045740496 @default.
• W4362453650 cites W3092222842 @default.
• W4362453650 cites W3096831136 @default.
• W4362453650 cites W3113152109 @default.
• W4362453650 cites W3129506774 @default.
• W4362453650 cites W3171093259 @default.
• W4362453650 cites W3175504132 @default.
• W4362453650 cites W3192378566 @default.
• W4362453650 cites W3197294349 @default.
• W4362453650 cites W3197911091 @default.
• W4362453650 cites W3204197025 @default.
• W4362453650 cites W3207009343 @default.
• W4362453650 cites W3212156041 @default.
• W4362453650 cites W3212398396 @default.
• W4362453650 cites W4200011012 @default.
• W4362453650 cites W4200492386 @default.
• W4362453650 cites W4220965924 @default.
• W4362453650 cites W4221023822 @default.
• W4362453650 cites W4224237033 @default.
• W4362453650 cites W4225400017 @default.
• W4362453650 cites W4225984668 @default.
• W4362453650 cites W4226095742 @default.
• W4362453650 cites W4281953486 @default.
• W4362453650 cites W4292315759 @default.
• W4362453650 cites W4293680188 @default.
• W4362453650 doi "https://doi.org/10.1016/j.engstruct.2023.116058" @default.
• W4362453650 hasPublicationYear "2023" @default.
• W4362453650 type Work @default.
• W4362453650 citedByCount "1" @default.
• W4362453650 crossrefType "journal-article" @default.
• W4362453650 hasAuthorship W4362453650A5004328892 @default.
• W4362453650 hasAuthorship W4362453650A5083516684 @default.
• W4362453650 hasAuthorship W4362453650A5089520262 @default.
• W4362453650 hasConcept C108583219 @default.
• W4362453650 hasConcept C115961682 @default.
• W4362453650 hasConcept C127413603 @default.
• W4362453650 hasConcept C153180895 @default.
• W4362453650 hasConcept C154945302 @default.
• W4362453650 hasConcept C160633673 @default.
• W4362453650 hasConcept C17744445 @default.
• W4362453650 hasConcept C199539241 @default.
• W4362453650 hasConcept C2776650193 @default.
• W4362453650 hasConcept C31972630 @default.
• W4362453650 hasConcept C39890363 @default.
• W4362453650 hasConcept C41008148 @default.
• W4362453650 hasConcept C50644808 @default.
• W4362453650 hasConcept C66938386 @default.
• W4362453650 hasConcept C81363708 @default.
• W4362453650 hasConcept C89600930 @default.
• W4362453650 hasConceptScore W4362453650C108583219 @default.
• W4362453650 hasConceptScore W4362453650C115961682 @default.
• W4362453650 hasConceptScore W4362453650C127413603 @default.
• W4362453650 hasConceptScore W4362453650C153180895 @default.
• W4362453650 hasConceptScore W4362453650C154945302 @default.
• W4362453650 hasConceptScore W4362453650C160633673 @default.
• W4362453650 hasConceptScore W4362453650C17744445 @default.
• W4362453650 hasConceptScore W4362453650C199539241 @default.
• W4362453650 hasConceptScore W4362453650C2776650193 @default.
• W4362453650 hasConceptScore W4362453650C31972630 @default.
• W4362453650 hasConceptScore W4362453650C39890363 @default.
• W4362453650 hasConceptScore W4362453650C41008148 @default.
• W4362453650 hasConceptScore W4362453650C50644808 @default.

• next