Small crack detection method for cylinder block based on improved SSD model research

2021 ◽  
Author(s):  
Lingju Kong ◽  
Cheng Sun ◽  
Weijie Li ◽  
Yaohua Deng
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Small Crack ◽  
Cylinder Block ◽  
Model Research ◽  
Block Based

scholarly journals A Real-Time Bridge Crack Detection Method Based on an Improved Inception-Resnet-v2 Structure

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Jinkang Wang ◽  
Xiaohui He ◽  
Shao Faming ◽  
Guanlin Lu ◽  
Hu Cong ◽  
...  
Keyword(s):  
Real Time ◽  
Crack Detection ◽  
Detection Method

scholarly journals Quantitative models for microstructure and thermal conductivity of vermicular graphite cast iron cylinder block based on cooling rate

China Foundry ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 52-59
Author(s):  
Qing-yi Liu ◽  
Xiao-fu Zhang ◽  
Yu-cheng Sun ◽  
Ai-long Jiang ◽  
Ji-chao Li ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Cast Iron ◽  
Cooling Rate ◽  
Cylinder Block ◽  
Quantitative Models ◽  
Graphite Cast Iron ◽  
Block Based

Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

2021 ◽  
pp. 136943322098663
Author(s):  
Diana Andrushia A ◽  
Anand N ◽  
Eva Lubloy ◽  
Prince Arulraj G
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Detection Method ◽  
Concrete Structures ◽  
Vital Role ◽  
Subjective Knowledge ◽  
Thermal Cracks ◽  
Reinforced Cement ◽  
Novel Method ◽  
Human Inspection

Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).

scholarly journals An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1581
Author(s):  
Xiaolong Chen ◽  
Jian Li ◽  
Shuowen Huang ◽  
Hao Cui ◽  
Peirong Liu ◽  
...  
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Multiple Scales ◽  
Point Clouds ◽  
Depth Image ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Concrete Crack ◽  
3D Point Clouds ◽  
2D Images

Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

A new rail crack detection method using LSTM network for actual application based on AE technology

2018 ◽  
Vol 142 ◽  
pp. 78-86 ◽  
Author(s):  
Xin Zhang ◽  
Zhongxian Zou ◽  
Kangwei Wang ◽  
Qiushi Hao ◽  
Yan Wang ◽  
...  
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Actual Application ◽  
Lstm Network

scholarly journals Blotch Detection in Archive Films Based on Visual Saliency Map

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yildiz Aydin ◽  
Bekir Dizdaroğlu
Keyword(s):  
Computational Complexity ◽  
Cultural Heritage ◽  
False Alarm ◽  
Detection Method ◽  
Visual Saliency ◽  
Saliency Map ◽  
Saliency Maps ◽  
Block Based ◽  
The Given ◽  
Blotch Detection

Degradations frequently occur in archive films that symbolize the historical and cultural heritage of a nation. In this study, the problem of detection blotches commonly encountered in archive films is handled. Here, a block-based blotch detection method is proposed based on a visual saliency map. The visual saliency map reveals prominent areas in an input frame and thus enables more accurate results in the blotch detection. A simple and effective visual saliency map method is taken into consideration in order to reduce computational complexity for the detection phase. After the visual saliency maps of the given frames are obtained, blotch regions are estimated by considered spatiotemporal patches—without the requirement for motion estimation—around the saliency pixels, which are subjected to a prethresholding process. Experimental results show that the proposed block-based blotch detection method provides a significant advantage with reducing false alarm rates over HOG feature (Yous and Serir, 2017), LBP feature (Yous and Serir, 2017), and regions-matching (Yous and Serir, 2016) methods presented in recent years.

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