scholarly journals RDD2020: An annotated image dataset for Automatic Road Damage Detection using Deep Learning

Data in Brief ◽  
2021 ◽  
pp. 107133
Author(s):  
Deeksha Arya ◽  
Hiroya Maeda ◽  
Sanjay Kumar Ghosh ◽  
Durga Toshniwal ◽  
Yoshihide Sekimoto
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Image Dataset

scholarly journals A Deep Learning-Based Framework for Damage Detection with Time Series

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Qun Yang ◽  
Dejian Shen ◽  
Wencai Du ◽  
Weijun Li
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Damage Detection

scholarly journals INFRASTRUCTURE DEGRADATION AND POST-DISASTER DAMAGE DETECTION USING ANOMALY DETECTING GENERATIVE ADVERSARIAL NETWORKS

2020 ◽  
Vol V-2-2020 ◽  
pp. 573-582 ◽  
Author(s):  
S. M. Tilon ◽  
F. Nex ◽  
D. Duarte ◽  
N. Kerle ◽  
G. Vosselman
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Essential Information ◽  
Urban Scenes ◽  
Adversarial Network ◽  
Collapsed Buildings ◽  
Post Disaster

Abstract. Degradation and damage detection provides essential information to maintenance workers in routine monitoring and to first responders in post-disaster scenarios. Despite advance in Earth Observation (EO), image analysis and deep learning techniques, the quality and quantity of training data for deep learning is still limited. As a result, no robust method has been found yet that can transfer and generalize well over a variety of geographic locations and typologies of damages. Since damages can be seen as anomalies, occurring sparingly over time and space, we propose to use an anomaly detecting Generative Adversarial Network (GAN) to detect damages. The main advantages of using GANs are that only healthy unannotated images are needed, and that a variety of damages, including the never before seen damage, can be detected. In this study we aimed to investigate 1) the ability of anomaly detecting GANs to detect degradation (potholes and cracks) in asphalt road infrastructures using Mobile Mapper imagery and building damage (collapsed buildings, rubble piles) using post-disaster aerial imagery, and 2) the sensitivity of this method against various types of pre-processing. Our results show that we can detect damages in urban scenes at satisfying levels but not on asphalt roads. Future work will investigate how to further classify the found damages and how to improve damage detection for asphalt roads.

scholarly journals Prototyping Artificial Intelligent Classifiers using Deep Learning

2020 ◽  
Author(s):  
Herman Njoroge Chege
Keyword(s):  
Deep Learning ◽  
Citizen Science ◽  
Parameter Tuning ◽  
Biological Species ◽  
Biodiversity Monitoring ◽  
Practical Applications ◽  
Wide Range ◽  
Image Dataset ◽  
Ecological Applications

Point 1: Deep learning algorithms are revolutionizing how hypothesis generation, pattern recognition, and prediction occurs in the sciences. In the life sciences, particularly biology and its subfields,  the use of deep learning is slowly but steadily increasing. However, prototyping or development of tools for practical applications remains in the domain of experienced coders. Furthermore, many tools can be quite costly and difficult to put together without expertise in Artificial intelligence (AI) computing. Point 2: We built a biological species classifier that leverages existing open-source tools and libraries. We designed the corresponding tutorial for users with basic skills in python and a small, but well-curated image dataset. We included annotated code in form of a Jupyter Notebook that can be adapted to any image dataset, ranging from satellite images, animals to bacteria. The prototype developer is publicly available and can be adapted for citizen science as well as other applications not envisioned in this paper. Point 3: We illustrate our approach with a case study of 219 images of 3 three seastar species. We show that with minimal parameter tuning of the AI pipeline we can create a classifier with superior accuracy. We include additional approaches to understand the misclassified images and to curate the dataset to increase accuracy. Point 4: The power of AI approaches is becoming increasingly accessible. We can now readily build and prototype species classifiers that can have a great impact on research that requires species identification and other types of image analysis. Such tools have implications for citizen science, biodiversity monitoring, and a wide range of ecological applications.

UNMANED INSPECTION ORIENTATED UAV BRIDGE INSPECTION AND DAMAGE DETECTION USING DEEP LEARNING

2018 ◽  
Vol 74 (2) ◽  
pp. I_62-I_74
Author(s):  
Yu TABATA ◽  
Ji DANG ◽  
Daijiro HARUTA ◽  
Ashish SHRESTHA ◽  
CHUN
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Bridge Inspection

scholarly journals Identifying Ethnics of People through Face Recognition: A Deep CNN Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Ahmed Jawad A. AlBdairi ◽  
Zhu Xiao ◽  
Mohammed Alghaili
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Research Community ◽  
Facial Features ◽  
New Model ◽  
Image Dataset ◽  
Deep Cnn

The interest in face recognition studies has grown rapidly in the last decade. One of the most important problems in face recognition is the identification of ethnics of people. In this study, a new deep learning convolutional neural network is designed to create a new model that can recognize the ethnics of people through their facial features. The new dataset for ethnics of people consists of 3141 images collected from three different nationalities. To the best of our knowledge, this is the first image dataset collected for the ethnics of people and that dataset will be available for the research community. The new model was compared with two state-of-the-art models, VGG and Inception V3, and the validation accuracy was calculated for each convolutional neural network. The generated models have been tested through several images of people, and the results show that the best performance was achieved by our model with a verification accuracy of 96.9%.

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