A deep transfer learning model for inclusion defect detection of aeronautics composite materials

2020 ◽  
Vol 252 ◽  
pp. 112681 ◽  
Author(s):  
Yanfeng Gong ◽  
Hongliang Shao ◽  
Jun Luo ◽  
Zhixue Li
Keyword(s):  
Composite Materials ◽  
Transfer Learning ◽  
Defect Detection ◽  
Learning Model

scholarly journals Hot-Rolled Steel Strip Surface Inspection Based on Transfer Learning Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hao Wu ◽  
Quanquan Lv
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Defect Detection ◽  
Surface Defects ◽  
Learning Model ◽  
Strip Surface ◽  
Neural Network Method ◽  
Steel Strips ◽  
Network Method

In the production process of steel strips, the detection of surface defects is very important. However, traditional methods of defect detection bring problems of low detection accuracy and dependence on subjective judgment. In this study, the surface defects of steel strips are detected by a classic convolutional neural network method that is improved by the use of a transfer learning model. This model has the advantages of shorter training time, faster convergence, and more accurate weight parameters. The transfer learning model obtained through experiments secures better results in defect detection than the classic convolutional neural network method, as its accuracy of training and testing has reached about 98%. Finally, a model based on a full convolutional neural network (FCN) is proposed for segmenting the defective areas of steel strips.

scholarly journals Transfer Learning of a Deep Learning Model for Exploring Tourists’ Urban Image Using Geotagged Photos

2021 ◽  
Vol 10 (3) ◽  
pp. 137
Author(s):  
Youngok Kang ◽  
Nahye Cho ◽  
Jiyoung Yoon ◽  
Soyeon Park ◽  
Jiyeon Kim
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Field Studies ◽  
Learning Model ◽  
Learning Technologies ◽  
Regional Study ◽  
Processing Technologies ◽  
Final Model ◽  
Urban Image ◽  
Deep Learning Model

Recently, as computer vision and image processing technologies have rapidly advanced in the artificial intelligence (AI) field, deep learning technologies have been applied in the field of urban and regional study through transfer learning. In the tourism field, studies are emerging to analyze the tourists’ urban image by identifying the visual content of photos. However, previous studies have limitations in properly reflecting unique landscape, cultural characteristics, and traditional elements of the region that are prominent in tourism. With the purpose of going beyond these limitations of previous studies, we crawled 168,216 Flickr photos, created 75 scenes and 13 categories as a tourist’ photo classification by analyzing the characteristics of photos posted by tourists and developed a deep learning model by continuously re-training the Inception-v3 model. The final model shows high accuracy of 85.77% for the Top 1 and 95.69% for the Top 5. The final model was applied to the entire dataset to analyze the regions of attraction and the tourists’ urban image in Seoul. We found that tourists feel attracted to Seoul where the modern features such as skyscrapers and uniquely designed architectures and traditional features such as palaces and cultural elements are mixed together in the city. This work demonstrates a tourist photo classification suitable for local characteristics and the process of re-training a deep learning model to effectively classify a large volume of tourists’ photos.

Developing Games Using a Principles-Based Approach

2011 ◽  
Vol 4 (2) ◽  
pp. 88
Author(s):  
Peter Baggetta
Keyword(s):  
New Zealand ◽  
Transfer Learning ◽  
Learning Model ◽  
Expertise Development ◽  
Teaching Games For Understanding ◽  
Theory To Practice ◽  
Tactical Decision

The Teaching Games for Understanding (TGfU) model was first developed by Bunker and Thorpe in 1982 as a model for coaches to help players become more skillful players. Since then other versions of the model have been developed such as the tactical decision-learning model (Grehaigne, Godbout, & Bouthier, 2001) in France and the game–sense approach (Australian Sports Commission, 1991) in Australia and New Zealand. The key aspect of all the models is the design of well-structured conditioned and modified games that require players to make decisions to develop their game understanding and tactical awareness. However, both novice and experienced coaches often struggle with connecting theory to practice especially in the area of creating and developing contextualized games that actually transfer learning from training to performance in games. In order to effectively create and use games that transfer learning, coaches can use a Principles-Based approach to develop games. The Principles-Based approach removes the dichotomy of traditional drills versus games and instead combines the drills approach with a games-context approach that links principles to skills that allow for increased individual and team expertise development. This presentation will first describe a model for developing and connecting principles, policies, tactics and skills for team play. Following this the presentation will then describe how to use the principles to create contextualized games that connect practices with performance and progresses novice players toward becoming more competent performers.

Transfer Learning of The ResNet-18 and DenseNet-121 Model Used to Diagnose Intracranial Hemorrhage in CT Scanning

2021 ◽  
Vol 27 ◽  
Author(s):  
Qi Zhou ◽  
Wenjie Zhu ◽  
Fuchen Li ◽  
Mingqing Yuan ◽  
Linfeng Zheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Intracranial Hemorrhage ◽  
Intraventricular Hemorrhage ◽  
Learning Model ◽  
Normal Group ◽  
Subdural Hemorrhage ◽  
Model Assessment ◽  
Learning Models ◽  
Deep Learning Model

Objective: To verify the ability of the deep learning model in identifying five subtypes and normal images in noncontrast enhancement CT of intracranial hemorrhage. Method: A total of 351 patients (39 patients in the normal group, 312 patients in the intracranial hemorrhage group) performed with intracranial hemorrhage noncontrast enhanced CT were selected, with 2768 images in total (514 images for the normal group, 398 images for the epidural hemorrhage group, 501 images for the subdural hemorrhage group, 497 images for the intraventricular hemorrhage group, 415 images for the cerebral parenchymal hemorrhage group, and 443 images for the subarachnoid hemorrhage group). Based on the diagnostic reports of two radiologists with more than 10 years of experience, the ResNet-18 and DenseNet-121 deep learning models were selected. Transfer learning was used. 80% of the data was used for training models, 10% was used for validating model performance against overfitting, and the last 10% was used for the final evaluation of the model. Assessment indicators included accuracy, sensitivity, specificity, and AUC values. Results: The overall accuracy of ResNet-18 and DenseNet-121 models were 89.64% and 82.5%, respectively. The sensitivity and specificity of identifying five subtypes and normal images were above 0.80. The sensitivity of DenseNet-121 model to recognize intraventricular hemorrhage and cerebral parenchymal hemorrhage was lower than 0.80, 0.73, and 0.76 respectively. The AUC values of the two deep learning models were above 0.9. Conclusion: The deep learning model can accurately identify the five subtypes of intracranial hemorrhage and normal images, and it can be used as a new tool for clinical diagnosis in the future.

scholarly journals Detecting Damage Building Using Real-Time Crowdsourced Images And Transfer Learning

2021 ◽  
Author(s):  
Gaurav Chachra ◽  
Qingkai Kong ◽  
Jim Huang ◽  
Srujay Korlakunta ◽  
Jennifer Grannen ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Learning Model ◽  
Research Community ◽  
Rescue Work ◽  
The Public ◽  
Social Media Platforms ◽  
Deep Learning Model

Abstract After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important locations on the images that facilitate the decision.

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