Performance Comparison of Pretrained Convolutional Neural Networks on Crack Detection in Buildings

2018 ◽  
Author(s):  
Ç. F. Özgenel ◽  
Arzu Gönenç Sorguç
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Crack Detection ◽  
Performance Comparison

scholarly journals SCCDNet: A Pixel-Level Crack Segmentation Network

2021 ◽  
Vol 11 (11) ◽  
pp. 5074
Author(s):  
Haotian Li ◽  
Zhuang Yue ◽  
Jingyu Liu ◽  
Yi Wang ◽  
Huaiyu Cai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Crack Detection ◽  
Feature Map ◽  
Connected Structure

Cracks are one of the most serious defects that threaten the safety of bridges. In order to detect different forms of cracks in different collection environments quickly and accurately, we proposed a pixel-level crack segmentation network based on convolutional neural networks, which is called the Skip Connected Crack Detection Network (SCCDNet). The network is composed of three parts: the Encoder module with 13 convolutional layers pretrained in the VGG-16 network, the Decoder module with a densely connected structure, and the Skip-Squeeze-and-Excitation (SSE) module which connects the feature map shaving the same resolution in the Encoder and Decoder. We used depthwise separable convolution to improve the accuracy of crack segmentation while reducing the complexity of the model. In this paper, a dataset containing cracks collected in different scenes was established, and SCCDNet was trained and tested on this dataset. Compared with the advanced models, SCCDNet obtained the best crack segmentation performance, while F-score reached 0.7763.

scholarly journals Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification

2020 ◽  
Vol 10 (19) ◽  
pp. 6940 ◽  
Author(s):  
Vincenzo Taormina ◽  
Donato Cascio ◽  
Leonardo Abbene ◽  
Giuseppe Raso
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fluorescence Intensity ◽  
Characteristic Curve ◽  
Performance Comparison ◽  
Fine Tuning ◽  
Tuning Method ◽  
Effective Performance ◽  
Training Modalities

The search for anti-nucleus antibodies (ANA) represents a fundamental step in the diagnosis of autoimmune diseases. The test considered the gold standard for ANA research is indirect immunofluorescence (IIF). The best substrate for ANA detection is provided by Human Epithelial type 2 (HEp-2) cells. The first phase of HEp-2 type image analysis involves the classification of fluorescence intensity in the positive/negative classes. However, the analysis of IIF images is difficult to perform and particularly dependent on the experience of the immunologist. For this reason, the interest of the scientific community in finding relevant technological solutions to the problem has been high. Deep learning, and in particular the Convolutional Neural Networks (CNNs), have demonstrated their effectiveness in the classification of biomedical images. In this work the efficacy of the CNN fine-tuning method applied to the problem of classification of fluorescence intensity in HEp-2 images was investigated. For this purpose, four of the best known pre-trained networks were analyzed (AlexNet, SqueezeNet, ResNet18, GoogLeNet). The classifying power of CNN was investigated with different training modalities; three levels of freezing weights and scratch. Performance analysis was conducted, in terms of area under the ROC (Receiver Operating Characteristic) curve (AUC) and accuracy, using a public database. The best result achieved an AUC equal to 98.6% and an accuracy of 93.9%, demonstrating an excellent ability to discriminate between the positive/negative fluorescence classes. For an effective performance comparison, the fine-tuning mode was compared to those in which CNNs are used as feature extractors, and the best configuration found was compared with other state-of-the-art works.

scholarly journals Differentiation Between Anteroposterior and Posteroanterior Chest X-Ray View Position With Convolutional Neural Networks

2020 ◽  
Author(s):  
René Hosch ◽  
Lennard Kroll ◽  
Felix Nensa ◽  
Sven Koitka
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Performance Comparison ◽  
University Hospital ◽  
Hospital Data ◽  
X Ray ◽  
Ensemble Models ◽  
Class Distribution ◽  
Meta Information ◽  
Chest X Ray

Purpose Detection and validation of the chest X-ray view position with use of convolutional neural networks to improve meta-information for data cleaning within a hospital data infrastructure. Material and Methods Within this paper we developed a convolutional neural network which automatically detects the anteroposterior and posteroanterior view position of a chest radiograph. We trained two different network architectures (VGG variant and ResNet-34) with data published by the RSNA (26 684 radiographs, class distribution 46 % AP, 54 % PA) and validated these on a self-compiled dataset with data from the University Hospital Essen (4507, radiographs, class distribution 55 % PA, 45 % AP) labeled by a human reader. For visualization and better understanding of the network predictions, a Grad-CAM was generated for each network decision. The network results were evaluated based on the accuracy, the area under the curve (AUC), and the F1-score against the human reader labels. Also a final performance comparison between model predictions and DICOM labels was performed. Results The ensemble models reached accuracy and F1-scores greater than 95 %. The AUC reaches more than 0.99 for the ensemble models. The Grad-CAMs provide insight as to which anatomical structures contributed to a decision by the networks which are comparable with the ones a radiologist would use. Furthermore, the trained models were able to generalize over mislabeled examples, which was found by comparing the human reader labels to the predicted labels as well as the DICOM labels. Conclusion The results show that certain incorrectly entered meta-information of radiological images can be effectively corrected by deep learning in order to increase data quality in clinical application as well as in research. Key Points:  Citation Format

scholarly journals Convolutional neural networks performance comparison for handwritten Bengali numerals recognition

2019 ◽  
Vol 1 (12) ◽  
Author(s):  
Md. Moklesur Rahman ◽  
Md. Shafiqul Islam ◽  
Roberto Sassi ◽  
Md. Aktaruzzaman
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Performance Comparison

Performance Comparison of Deep Convolutional Neural Networks for Vespa Image Recognition

2019 ◽  
Vol 34 (3) ◽  
pp. 207-215 ◽  
Author(s):  
Cheol-Hee Lee ◽  
Yoon-Ju Jeong ◽  
Taeho Kim ◽  
Jae-Hyeon Park ◽  
Seongbin Bak ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Performance Comparison ◽  
Deep Convolutional Neural Networks
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