4.7 Article

Automatic concrete crack segmentation model based on transformer

Related references

Note: Only part of the references are listed.
Article Engineering, Multidisciplinary

A research on an improved Unet-based concrete crack detection algorithm

Lingxin Zhang et al.

Summary: The paper proposed the CrackUnet model based on deep learning, which uses resized, labeled, and augmented crack images to create a dataset, and adopts a new loss function called generalized Dice loss to improve crack detection accuracy. The study investigates the impact of dataset size and model depth on training time, detection accuracy, and speed, showing that the CrackUnet model outperforms other methods with strong robustness and generalization.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2021)

Article Construction & Building Technology

Intelligent crack detection based on attention mechanism in convolution neural network

Xiaoning Cui et al.

Summary: In this study, the proposed Att-Unet model achieved better results in crack segmentation tasks, showing improved accuracy, precision, and F1-scores. Att-Unet effectively extracts multi-scale features of cracks, focuses on critical areas, and reconstructs semantics to improve crack segmentation capability.

ADVANCES IN STRUCTURAL ENGINEERING (2021)

Article Construction & Building Technology

Attention-guided analysis of infrastructure damage with semi-supervised deep learning

Enes Karaaslan et al.

Summary: This paper proposes a novel method to improve the accuracy of damage quantification through attention-guided technique, utilizing a fast object detection model to perform real-time crack and spall detection and working interactively with human inspectors for effective recognition of the region of interest. Such approach leads to 30% more precision with negligible impact on computational speed compared to traditional analysis methods.

AUTOMATION IN CONSTRUCTION (2021)

Article Construction & Building Technology

A deep learning approach for fast detection and classification of concrete damage

Yongqing Jiang et al.

Summary: This research has contributed to solving the key issues in concrete damage detection tasks by preparing a concrete damage data set and optimizing the object detection algorithm, which has resulted in improved inference speed and detection accuracy.

AUTOMATION IN CONSTRUCTION (2021)

Article Construction & Building Technology

Semi-supervised semantic segmentation network for surface crack detection

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 Automation & Control Systems

Automatic pixel-level crack segmentation in images using fully convolutional neural network based on residual blocks and pixel local weights

Raza Ali et al.

Summary: Deep learning has been applied to automated crack detection and identification processes. In the case of data imbalance, introducing a local weighting factor and sensitivity map can improve the accuracy of crack pixel segmentation. Based on the newly established MSCI dataset, the proposed method shows excellent performance in crack pixel and non-crack pixel accuracy.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2021)

Article Engineering, Civil

Deep Learning-Based Real-Time Crack Segmentation for Pavement Images

Wenjun Wang et al.

Summary: In this study, a lightweight crack segmentation model is proposed that strikes a balance between inference speed and segmentation performance. The model effectively identifies cracks with superior results, able to process large images and save time in real-time.

KSCE JOURNAL OF CIVIL ENGINEERING (2021)

Article Construction & Building Technology

Densely connected deep neural network considering connectivity of pixels for automatic crack detection

Qipei Mei et al.

AUTOMATION IN CONSTRUCTION (2020)

Article Engineering, Civil

Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection

Fan Yang et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2020)

Article Automation & Control Systems

SDDNet: Real-Time Crack Segmentation

Wooram Choi et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)

Article Construction & Building Technology

Artificial intelligence-empowered pipeline for image-based inspection of concrete structures

Jun Kang Chow et al.

AUTOMATION IN CONSTRUCTION (2020)

Article Computer Science, Interdisciplinary Applications

Concrete crack detection using context-aware deep semantic segmentation network

Xinxiang Zhang et al.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2019)

Article Computer Science, Interdisciplinary Applications

Multi-class structural damage segmentation using fully convolutional networks

Juan Jose Rubio et al.

COMPUTERS IN INDUSTRY (2019)

Article Construction & Building Technology

Pixel-level crack delineation in images with convolutional feature fusion

FuTao Ni et al.

STRUCTURAL CONTROL & HEALTH MONITORING (2019)

Article Engineering, Multidisciplinary

Deep learning-based autonomous concrete crack evaluation through hybrid image scanning

Keunyoung Jang et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2019)

Article Computer Science, Theory & Methods

A survey on Image Data Augmentation for Deep Learning

Connor Shorten et al.

JOURNAL OF BIG DATA (2019)

Article Engineering, Aerospace

Robust Concrete Crack Detection Using Deep Learning-Based Semantic Segmentation

Donghan Lee et al.

INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES (2019)

Article Automation & Control Systems

Automated Crack Detection on Concrete Bridges

Prateek Prasanna et al.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2016)

Article Computer Science, Interdisciplinary Applications

Robust Automated Concrete Damage Detection Algorithms for Field Applications

David Lattanzi et al.

JOURNAL OF COMPUTING IN CIVIL ENGINEERING (2014)

Article Computer Science, Interdisciplinary Applications

Texture Analysis Based Damage Detection of Ageing Infrastructural Elements

Michael O'Byrne et al.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2013)