Vision-Based Fatigue Crack Detection of Steel Structures Using Video Feature Tracking

2018 ◽  
Vol 33 (9) ◽  
pp. 783-799 ◽  
Author(s):  
Xiangxiong Kong ◽  
Jian Li
Keyword(s):  
Fatigue Crack ◽  
Feature Tracking ◽  
Crack Detection ◽  
Steel Structures ◽  
Video Feature ◽  
Fatigue Crack Detection

P107 Fatigue Crack Detection in Steel Structures by Self-reference Lock-in Thermography

2005 ◽  
Vol 2005 (0) ◽  
pp. 637-638
Author(s):  
Takashi Nishimura ◽  
Takahide Sakagami ◽  
Shiro Kubo ◽  
Kazunari Ishino
Keyword(s):  
Fatigue Crack ◽  
Crack Detection ◽  
Steel Structures ◽  
Fatigue Crack Detection ◽  
Lock In ◽  
Self Reference

scholarly journals Pixel-Level Fatigue Crack Segmentation in Large-Scale Images of Steel Structures Using an Encoder–Decoder Network

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4135
Author(s):  
Chuanzhi Dong ◽  
Liangding Li ◽  
Jin Yan ◽  
Zhiming Zhang ◽  
Hong Pan ◽  
...  
Keyword(s):  
Fatigue Crack ◽  
Large Scale ◽  
Crack Detection ◽  
Fatigue Cracks ◽  
Steel Structures ◽  
Life Cycles ◽  
Detection Methods ◽  
Destructive Testing ◽  
Existing Structures ◽  
Fatigue Crack Detection

Fatigue cracks are critical types of damage in steel structures due to repeated loads and distortion effects. Fatigue crack growth may lead to further structural failure and even induce collapse. Efficient and timely fatigue crack detection and segmentation can support condition assessment, asset maintenance, and management of existing structures and prevent the early permit post and improve life cycles. In current research and engineering practices, visual inspection is the most widely implemented approach for fatigue crack inspection. However, the inspection accuracy of this method highly relies on the subjective judgment of the inspectors. Furthermore, it needs large amounts of cost, time, and labor force. Non-destructive testing methods can provide accurate detection results, but the cost is very high. To overcome the limitations of current fatigue crack detection methods, this study presents a pixel-level fatigue crack segmentation framework for large-scale images with complicated backgrounds taken from steel structures by using an encoder-decoder network, which is modified from the U-net structure. To effectively train and test the images with large resolutions such as 4928 × 3264 pixels or larger, the large images were cropped into small images for training and testing. The final segmentation results of the original images are obtained by assembling the segment results in the small images. Additionally, image post-processing including opening and closing operations were implemented to reduce the noises in the segmentation maps. The proposed method achieved an acceptable accuracy of automatic fatigue crack segmentation in terms of average intersection over union (mIOU). A comparative study with an FCN model that implements ResNet34 as backbone indicates that the proposed method using U-net could give better fatigue crack segmentation performance with fewer training epochs and simpler model structure. Furthermore, this study also provides helpful considerations and recommendations for researchers and practitioners in civil infrastructure engineering to apply image-based fatigue crack detection.

scholarly journals The early stage wheel fatigue crack detection using eddy current pulsed thermography

2017 ◽  
Author(s):  
Jianping Peng ◽  
Kang Zhang ◽  
Kai Yang ◽  
Zhu He ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Fatigue Crack ◽  
Eddy Current ◽  
Crack Detection ◽  
Early Stage ◽  
Pulsed Thermography ◽  
Fatigue Crack Detection
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