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Article
Computer Science, Interdisciplinary Applications
Jingjing Guo et al.
Summary: This study proposes an informativeness-guided active learning methodology to select informative data for training a robust facade defects detector. A data annotation workflow is developed, and an active learning-based model training strategy is adopted to improve detection performance. An innovative informativeness assessment method combining uncertainty and representativeness is proposed.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Fahad Shamshad et al.
Summary: This survey reviews the applications of Transformers in medical imaging, covering tasks such as medical image segmentation, detection, classification, restoration, synthesis, registration, and clinical report generation. The challenges and solutions for each application are discussed, and future research directions are highlighted. The survey aims to spark interest in the academic community and provide researchers with an up-to-date reference regarding the applications of Transformer models in medical imaging.
MEDICAL IMAGE ANALYSIS
(2023)
Article
Construction & Building Technology
Chao Xiang et al.
Summary: This study proposes a dual-encoder network called DTrC-Net, which combines transformers and convolutional neural networks, to address the challenges in crack segmentation caused by complex scenes. The DTrC-Net captures both local features and global contextual information of crack images and enhances feature fusion between adjacent and codec layers. Experimental results show that DTrC-Net achieves better predictions than other segmentation networks, with high accuracy (75.60%), recall (78.86%), F1-score (76.44%), and intersection over union (64.30%) on the Crack3238 dataset. It also achieves a fast processing speed of 78 frames per second.
AUTOMATION IN CONSTRUCTION
(2023)
Article
Computer Science, Interdisciplinary Applications
Pang-Jo Chun et al.
Summary: This study introduces a deep learning model with an attention mechanism to generate descriptive sentences about bridge damage from images, achieving high accuracy. The algorithm is capable of outputting multiple sentences to interpret complex images, with better results compared to models without the attention mechanism. The developed method enables user-friendly text-based explanations of bridge damage in images, benefiting engineers and even administrative staff.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2022)
Article
Engineering, Industrial
Jingjing Guo et al.
Summary: This research quantitatively evaluates human-related activities and factors in automation-enabled facade visual inspection through a Delphi study and extracts the most critical ones influencing uncertainty. It contributes to facilitating understanding of uncertainty in human-cyber-physical systems and providing effective recommendations for uncertainty control in automation-enabled facade visual inspection.
JOURNAL OF MANAGEMENT IN ENGINEERING
(2022)
Article
Construction & Building Technology
Chao Xiang et al.
Summary: This paper proposes an automatic microcrack detection method based on super-resolution reconstruction and semantic segmentation. By using deep learning for super-resolution reconstruction and semantic segmentation, the accuracy of crack detection and feature quantification is significantly improved.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Construction & Building Technology
Elyas Asadi Shamsabadi et al.
Summary: This research proposes a ViT-based framework for crack detection on asphalt and concrete surfaces, achieving enhanced real-world crack segmentation performance through transfer learning and IoU loss function. Compared to CNN-based models, TransUNet with a CNN-ViT backbone shows better average IoU on small and multiscale crack semantics and ViT helps the encoder-decoder network exhibit robust performance against various noisy signals.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Computer Science, Interdisciplinary Applications
Honghu Chu et al.
Summary: This study proposes a multiscale feature fusion network named Tiny-Crack-Net (TCN) to address the class imbalance and limited local receptive field issues in crack segmentation. By modifying the residual network, incorporating dual attention modules, and implementing multiscale fusion operations, the network's ability to segment tiny cracks is significantly enhanced. Evaluation on an open-source dataset and field test results validate the effectiveness and robustness of Tiny-Crack-Net.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2022)
Article
Engineering, Civil
Jie Fang et al.
Summary: This paper proposes an external attention based TransUNet for crack detection, which improves the performance and robustness of the framework through skip connections to propagate texture information and model long-range dependency.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Green & Sustainable Science & Technology
Mohammad Sadegh Barkhordari et al.
Summary: This paper investigates the efficiency of computer-vision hybrid models for automatically detecting damage to reinforced concrete elements. The best-performing model, using the WSN-SVM algorithm, achieved an accuracy of 99.1% in classifying the damage.
Article
Computer Science, Interdisciplinary Applications
Pang-jo Chun et al.
Summary: This paper introduces an automated crack detection method based on image processing using LightGBM, a supervised machine learning method, to accurately identify appropriate features for crack detection.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2021)
Review
Engineering, Multidisciplinary
Chuan-Zhi Dong et al.
Summary: Computer vision technologies have gained significant attention in structural health monitoring, providing advantages such as non-contact, long distance, rapid, low cost applications. Most research focuses on the implementation and integration of two-dimensional computer vision techniques to solve structural health monitoring problems.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2021)
Article
Construction & Building Technology
Jingjing Guo et al.
Summary: The study proposes a rule-based deep learning method for evaluation-oriented facade defects detection, which improves the performance of detection to meet the requirement of condition evaluation. The method is adaptable to various distance settings in the requirement.
AUTOMATION IN CONSTRUCTION
(2021)
Article
Computer Science, Artificial Intelligence
Jingdong Wang et al.
Summary: The High-Resolution Network (HRNet) maintains high-resolution representations and exchanges information across resolutions, resulting in superior performance in various applications such as human pose estimation, semantic segmentation, and object detection.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Construction & Building Technology
Wenjun Wang et al.
Summary: This paper proposes a semi-supervised semantic segmentation network for crack detection, which reduces the requirement of annotated data and improves the model accuracy through the collaboration of student and teacher models. It can reduce the annotation workload while maintaining high accuracy.
AUTOMATION IN CONSTRUCTION
(2021)
Article
Computer Science, Interdisciplinary Applications
Shang Jiang et al.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2020)
Article
Chemistry, Analytical
Mohsen Azimi et al.
Review
Construction & Building Technology
Limin Sun et al.
JOURNAL OF STRUCTURAL ENGINEERING
(2020)
Article
Chemistry, Multidisciplinary
Lu Deng et al.
APPLIED SCIENCES-BASEL
(2020)
Article
Construction & Building Technology
Shanglian Zhou et al.
AUTOMATION IN CONSTRUCTION
(2020)
Review
Computer Science, Interdisciplinary Applications
Yung-An Hsieh et al.
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
(2020)
Article
Engineering, Multidisciplinary
Hyunjun Kim et al.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2019)
Article
Computer Science, Artificial Intelligence
Qin Zou et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2019)
Article
Computer Science, Interdisciplinary Applications
Somin Park et al.
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
(2019)
Article
Computer Science, Artificial Intelligence
Vijay Badrinarayanan et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2017)
Article
Computer Science, Interdisciplinary Applications
Young-Jin Cha et al.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2017)
Article
Engineering, Electrical & Electronic
Li Deng et al.
FOUNDATIONS AND TRENDS IN SIGNAL PROCESSING
(2013)
