External Attention Based TransUNet and Label Expansion Strategy for Crack Detection

Fang, Jie; Yang, Chunxu; Shi, Yuetian; Wang, Nan; Zhao, Yangyang

doi:10.1109/tits.2022.3154407

Cited by 41 publications

(10 citation statements)

References 36 publications

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“…Chen et al [53] proposed the TransUNet for medical image segmentation, which merits both transformers and U-Net. Fang et al [54] presented the external attention-based TransUNet for crack detection. In addition, transformer-based methods were also used in remote sensing images.…”

Section: Transformer-based Methods In CVmentioning

confidence: 99%

Context and Difference Enhancement Network for Change Detection

Song

Dong

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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At present, convolution neural networks have achieved good performance in remote sensing image change detection. However, due to the locality of convolution, these methods are difficult to capture the global context relationships among different-level features. To alleviate this issue, we propose a context and difference enhancement network (CDENet) for change detection, which can strongly model global context relationships and enhance the change difference. Specifically, our backbone is the dual TransUNet, which is based on U-Net and equipped with transformer block in the encoder. The dual TransUNet is used to extract bi-temporal features. Then, the features are encoded as the input sequence, which is conducive to modeling the global context. Moreover, we design the content difference enhancement module to process the dual features of each layer in the encoder. The designed module can increase the spatial attention of difference regions to enhance the change difference features. In the decoder, we adopt a simple crosslayer feature fusion to combine the upsampled features with the high-resolution features, which is used to generate more accurate results. Finally, we adopt a novel loss to supervise the accuracy of results in regions and pixels. The experiments on two public change detection datasets demonstrate that our CDENet has strong competitiveness and performs better than the state-ofthe-art methods.

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Section: Transformer-based Methods In CVmentioning

confidence: 99%

Context and Difference Enhancement Network for Change Detection

Song

Dong

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…However, the most common CNN denoising model that is based on full-connection architecture often encounters some shortcoming to be solved, such as being unable to effectively remove the relative broadband noise, requiring a lot of training time and a large number of training samples to be effective ( Zhang et al, 2018 ). In this work, several new CNN models, namely, the fast and flexible denoising convolutional neural network (FFDNet) and Pyramid Real Image Denoising Network (PRIDNet), for flexible, effective, and fast discriminative denoising, the PRIDNet is specifically presented in detail for blind denoising of cell images through three sequential stages ( Al-Kofahi et al, 2018 ; Chen et al, 2021b ; Fang et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Pixel-level multimodal fusion deep networks for predicting subcellular organelle localization from label-free live-cell imaging

et al. 2022

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Complex intracellular organizations are commonly represented by dividing the metabolic process of cells into different organelles. Therefore, identifying sub-cellular organelle architecture is significant for understanding intracellular structural properties, specific functions, and biological processes in cells. However, the discrimination of these structures in the natural organizational environment and their functional consequences are not clear. In this article, we propose a new pixel-level multimodal fusion (PLMF) deep network which can be used to predict the location of cellular organelle using label-free cell optical microscopy images followed by deep-learning-based automated image denoising. It provides valuable insights that can be of tremendous help in improving the specificity of label-free cell optical microscopy by using the Transformer–Unet network to predict the ground truth imaging which corresponds to different sub-cellular organelle architectures. The new prediction method proposed in this article combines the advantages of a transformer’s global prediction and CNN’s local detail analytic ability of background features for label-free cell optical microscopy images, so as to improve the prediction accuracy. Our experimental results showed that the PLMF network can achieve over 0.91 Pearson’s correlation coefficient (PCC) correlation between estimated and true fractions on lung cancer cell-imaging datasets. In addition, we applied the PLMF network method on the cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new way for the time-resolved study of subcellular components in different cells, especially for cancer cells.

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“…Fang et al [ 31 ] proposed an attention-based TransUNet for crack detection in road surfaces. The TransUNet takes the detailed texture information of detected cracks from the shallow layers and passes it to deep layers through skip connections.…”

Section: Related Workmentioning

confidence: 99%

Predictive Maintenance of Norwegian Road Network Using Deep Learning Models

Hassan

Steinnes

Gustafsson

et al. 2023

Sensors

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Industry 4.0 has revolutionized the use of physical and digital systems while playing a vital role in the digitalization of maintenance plans for physical assets in an optimal way. Road network conditions and timely maintenance plans are essential in the predictive maintenance (PdM) of a road. We developed a PdM-based approach that uses pre-trained deep learning models to recognize and detect the road crack types effectively and efficiently. We, in this work, explore the use of deep neural networks to classify roads based on the amount of deterioration. This is done by training the network to identify various types of cracks, corrugation, upheaval, potholes, and other types of road damage. Based on the amount and severity of the damage, we can determine the degradation percentage and have a PdM framework where we can identify the intensity of damage occurrence and, thus, prioritize the maintenance decisions. The inspection authorities and stakeholders can make maintenance decisions for certain types of damages using our deep learning-based road predictive maintenance framework. We evaluated our approach using precision, recall, F1-score, intersection-over-union, structural similarity index, and mean average precision measures, and found that our proposed framework achieved significant performance.

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External Attention Based TransUNet and Label Expansion Strategy for Crack Detection

Cited by 41 publications

References 36 publications

Context and Difference Enhancement Network for Change Detection

Context and Difference Enhancement Network for Change Detection

Pixel-level multimodal fusion deep networks for predicting subcellular organelle localization from label-free live-cell imaging

Predictive Maintenance of Norwegian Road Network Using Deep Learning Models

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