DEVELOPMENT OF THE DEEP LEARNING BASED DAMAGE DETECTION MODEL FOR BUILDINGS UTILIZING AERIAL PHOTOGRAPHS OF MULTIPLE EARTHQUAKES

Automatic damage detection using deep learning warrants an extensive data source that captures complex pavement conditions. This paper proposes a thermal-RGB fusion image-based pavement damage detection model, wherein the fused RGB-thermal image is formed through multi-source sensor information to achieve fast and accurate defect detection including complex pavement conditions. The proposed method uses pre-trained EfficientNet B4 as the backbone architecture and generates an argument dataset (containing non-uniform illumination, camera noise, and scales of thermal images too) to achieve high pavement damage detection accuracy. This paper tests separately the performance of different input data (RGB, thermal, MSX, and fused image) to test the influence of input data and network on the detection results. The results proved that the fused image’s damage detection accuracy can be as high as 98.34% and by using the dataset after augmentation, the detection model deems to be more stable to achieve 98.35% precision, 98.34% recall, and 98.34% F1-score.

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Recognition of classroom student state features based on deep learning algorithms and machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189232 ◽

2020 ◽

pp. 1-12

Author(s):

Hu Jingchao ◽

Haiying Zhang

Keyword(s):

Deep Learning ◽

Feature Recognition ◽

Subjective Evaluation ◽

Recognition Algorithm ◽

Video Data ◽

State Recognition ◽

Detection Model ◽

State Classification ◽

Intelligent Models ◽

Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

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

Data in Brief ◽

10.1016/j.dib.2021.107133 ◽

2021 ◽

pp. 107133

Author(s):

Deeksha Arya ◽

Hiroya Maeda ◽

Sanjay Kumar Ghosh ◽

Durga Toshniwal ◽

Yoshihide Sekimoto

Keyword(s):

Deep Learning ◽

Damage Detection ◽

Image Dataset

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Hurricane Damage Detection using Machine Learning and Deep Learning Techniques: A Review

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1022/1/012035 ◽

2021 ◽

Vol 1022 ◽

pp. 012035

Author(s):

Swapandeep Kaur ◽

Sheifali Gupta ◽

Swati Singh

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Damage Detection ◽

Hurricane Damage ◽

Learning Techniques

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Machine Learning-Based Malicious X.509 Certificates’ Detection

Applied Sciences ◽

10.3390/app11052164 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2164

Author(s):

Jiaxin Li ◽

Zhaoxin Zhang ◽

Changyong Guo

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Ensemble Learning ◽

Traffic Analysis ◽

Learning Models ◽

Detection Model ◽

Analysis Tools ◽

Average Accuracy ◽

Machine Learning Models

X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.

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

IEEE Access ◽

10.1109/access.2021.3076436 ◽

2021 ◽

pp. 1-1

Author(s):

Qun Yang ◽

Dejian Shen ◽

Wencai Du ◽

Weijun Li

Keyword(s):

Time Series ◽

Deep Learning ◽

Damage Detection

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Automated Counting Grains on the Rice Panicle Based on Deep Learning Method

Sensors ◽

10.3390/s21010281 ◽

2021 ◽

Vol 21 (1) ◽

pp. 281

Author(s):

Ruoling Deng ◽

Ming Tao ◽

Xunan Huang ◽

Kemoh Bangura ◽

Qian Jiang ◽

...

Keyword(s):

Deep Learning ◽

Model Performance ◽

Rice Breeding ◽

Grain Number ◽

Lighting Condition ◽

Detection Model ◽

Grain Moisture ◽

Feature Pyramid ◽

Moisture Conditions ◽

Rice Panicles

Grain number per rice panicle, which directly determines grain yield, is an important agronomic trait for rice breeding and yield-related research. However, manually counting grains of rice per panicle is time-consuming, laborious, and error-prone. In this research, a grain detection model was proposed to automatically recognize and count grains on primary branches of a rice panicle. The model used image analysis based on deep learning convolutional neural network (CNN), by integrating the feature pyramid network (FPN) into the faster R-CNN network. The performance of the grain detection model was compared to that of the original faster R-CNN model and the SSD model, and it was found that the grain detection model was more reliable and accurate. The accuracy of the grain detection model was not affected by the lighting condition in which images of rice primary branches were taken. The model worked well for all rice branches with various numbers of grains. Through applying the grain detection model to images of fresh and dry branches, it was found that the model performance was not affected by the grain moisture conditions. The overall accuracy of the grain detection model was 99.4%. Results demonstrated that the model was accurate, reliable, and suitable for detecting grains of rice panicles with various conditions.

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