scholarly journals A deep learning technique-based automatic monitoring method for experimental urban road inundation

2021 ◽  
Author(s):  
Hao Han ◽  
Jingming Hou ◽  
Ganggang Bai ◽  
Bingyao Li ◽  
Tian Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Monitoring ◽  
Water Area ◽  
Detection Accuracy ◽  
Accuracy Rate ◽  
Validation Data ◽  
Monitoring Method ◽  
Urban Flood ◽  
Urban Road ◽  
Average Accuracy

Abstract Reports indicate that high-cost, insecurity, and difficulty in complex environments hinder the traditional urban road inundation monitoring approach. This work proposed an automatic monitoring method for experimental urban road inundation based on the YOLOv2 deep learning framework. The proposed method is an affordable, secure, with high accuracy rates in urban road inundation evaluation. The automatic detection of experimental urban road inundation was carried out under both dry and wet conditions on roads in the study area with a scale of few m2. The validation average accuracy rate of the model was high with 90.1% inundation detection, while its training average accuracy rate was 96.1%. This indicated that the model has effective performance with high detection accuracy and recognition ability. Besides, the inundated water area of the experimental inundation region and the real road inundation region in the images was computed, showing that the relative errors of the measured area and the computed area were less than 20%. The results indicated that the proposed method can provide reliable inundation area evaluation. Therefore, our findings provide an effective guide in the management of urban floods and urban flood-warning, as well as systematical validation data for hydrologic and hydrodynamic models.

scholarly journals Pendeteksian Septoria pada Tanaman Tomat dengan Metode Deep Learning berbasis Raspberry Pi

2021 ◽  
Vol 5 (1) ◽  
pp. 107-113
Author(s):  
Kahlil Muchtar ◽  
Chairuman ◽  
Yudha Nurdin ◽  
Afdhal Afdhal
Keyword(s):  
Deep Learning ◽  
Leaf Spot ◽  
Raspberry Pi ◽  
Leaf Spot Disease ◽  
Detection Accuracy ◽  
Crop Failure ◽  
Tomato Production ◽  
Spot Disease ◽  
Average Accuracy ◽  
Septoria Leaf Spot

much needed to meet the needs of both industry and households. However, tomato plants still require serious handling in increasing the yields. Data from the Central Bureau of Statistics shows that the number of tomatoes produced is not in accordance with a large number of market demands, resulting from the decrease of tomato yields. One of the obstacles in increasing tomato production is that the crops are attacked by septoria leaf spot disease due to the fungus or the fungus Septoria Lycopersici Speg. Most farmers have limited knowledge of the early symptoms, which are not obvious, and also facing difficulty in detecting this disease earlier. The problem has been causing disadvantages such as crop failure or plant death. Based on this problem, a study will be conducted with the aim of designing a tool that can be used to detect septoria leaf spot disease based on deep learning using the Convolutional Neural Network (ConvNets or CNN) model, where an algorithm that resembles human nerves is one of the supervised learning and widely used for solving linear and non-linear problems. In addition, the researcher used the Raspberry Pi as a microcontroller and used the Intel Movidius Neural Computing Stick (NCS) which functions to speed up the computing process so that the detection process is easier because of its portable, fast and accurate nature. The average accuracy rate is 95.89% with detection accuracy between 84.22% to 100%.  

scholarly journals MalDeep: A Deep Learning Classification Framework against Malware Variants Based on Texture Visualization

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yuntao Zhao ◽  
Chunyu Xu ◽  
Bo Bo ◽  
Yongxin Feng
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Feature Space ◽  
Image Texture ◽  
Accuracy Rate ◽  
Texture Representation ◽  
Malware Classification ◽  
Classification Framework ◽  
Average Accuracy ◽  
New Feature

The increasing sophistication of malware variants such as encryption, polymorphism, and obfuscation calls for the new detection and classification technology. In this paper, MalDeep, a novel malware classification framework of deep learning based on texture visualization, is proposed against malicious variants. Through code mapping, texture partitioning, and texture extracting, we can study malware classification in a new feature space of image texture representation without decryption and disassembly. Furthermore, we built a malware classifier on convolutional neural network with two convolutional layers, two downsampling layers, and many full connection layers. We adopt the dataset, from Microsoft Malware Classification Challenge including 9 categories of malware families and 10868 variant samples, to train the model. The experiment results show that the established MalDeep has a higher accuracy rate for malware classification. In particular, for some backdoor families, the classification accuracy of the model reaches over 99%. Moreover, compared with other main antivirus software, MalDeep also outperforms others in the average accuracy for the variants from different families.

scholarly journals Research on Detection and Recognition of Abnormal Data caused by Network Intrusion using Deep Learning

2021 ◽  
Author(s):  
Yan Jian ◽  
Xiaoyang Dong ◽  
Liang Jian
Keyword(s):  
Deep Learning ◽  
False Alarm ◽  
False Alarm Rate ◽  
Data Sets ◽  
Accuracy Rate ◽  
Data Set ◽  
Network Intrusion ◽  
Average Accuracy ◽  
Learning Machine ◽  
Detection And Recognition

Abstract Based on deep learning, this study combined sparse autoencoder (SAE) with extreme learning machine (ELM) to design an SAE-ELM method to reduce the dimension of data features and realize the classification of different types of data. Experiments were carried out on NSL-KDD and UNSW-NB2015 data sets. The results showed that, compared with the K-means algorithm and the SVM algorithm, the proposed method had higher performance. On the NSL-KDD data set, the average accuracy rate of the SAE-ELM method was 98.93%, the false alarm rate was 0.17%, and the missing report rate was 5.36%. On the UNSW-NB2015 data set, the accuracy rate of the SAE-ELM method was 98.88%, the false alarm rate was 0.12%, and the missing report rate was 4.31%. The results show that the SAE-ELM method is effective in the detection and recognition of abnormal data and can be popularized and applied.

scholarly journals Deep Learning-based Automatic Monitoring of Pigs Physico-temporal Activities at Different Greenhouse Gas Concentrations

2021 ◽  
Author(s):  
Anil Bhujel ◽  
Elanchezhian Arulmozhi ◽  
Byeong Eun Moon ◽  
Hyeon Tae Kim
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Automatic Monitoring ◽  
Physical Activities ◽  
Detection Accuracy ◽  
Tracking Accuracy ◽  
Short Term ◽  
Health And Wellbeing ◽  
High Detection ◽  
Tracking Model

Pig behavior is an integral part of health and welfare management, as pigs usually reflect their inner emotions through behavior change. The livestock environment plays a key role in pigs' health and wellbeing. A poor farm environment increases the toxic GHGs, which might deteriorate pigs' health and welfare. In this study a computer-vision-based automatic monitoring and tracking model was proposed to detect short-term pigs' physical activities in a compromised environment. The ventilators of the livestock barn were closed for an hour, three times in a day (07:00-08:00, 13:00-14:00, and 20:00-21:00) to create a compromised environment, which increases the GHGs level significantly. The corresponding pig activities were observed before, during, and after an hour of the treatment. Two widely used object detection models (YOLOv4 and Fast-er R-CNN) were trained and compared their performances in terms of pig localization and posture detection. The YOLOv4, which outperformed the Faster R-CNN model, coupled with a Deep-SORT tracking algorithm to detect and track the pig activities. The results showed that the pigs became more inactive with the increase in GHG concentration, reducing their standing and walking activities. Moreover, the pigs also shortened their sternal-lying posture increasing the lateral lying posture duration at higher GHG concentration. The high detection accuracy (mAP: 98.67%) and tracking accuracy (MOTA: 93.86% and MOTP: 82.41%) signify the models’ efficacy in monitoring and tracking pigs' physical activities non-invasively.

scholarly journals Research on GPR image recognition based on deep learning

2020 ◽  
Vol 309 ◽  
pp. 03027
Author(s):  
Zhimin Gong ◽  
Huaiqing Zhang
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Ground Penetrating Radar ◽  
Simulation Software ◽  
Accuracy Rate ◽  
Data Set ◽  
Average Accuracy ◽  
Recognition Ability ◽  
Ground Penetrating ◽  
Traditional Image

It is difficult for traditional image recognition methods to accurately identify ground penetrating radar (GPR) images. This paper proposes a deep-learning based Faster R-CNN algorithm for the automatic classification and recognition of GPR images. Firstly, GPR images with different features were obtained by using gprMax, a professional GPR simulation software. Then, the feature of the target in the image was taken as the recognition object and the data set was made. Finally, Faster R-CNN’s recognition ability of GPR images was analyzed from various accuracy, average accuracy and other indicators. The results showed that Faster R-CNN could successfully identify GPR images and accurately classify them, with an average accuracy rate of 93.9%.

scholarly journals Deep Learning Model for the Inspection of Coffee Bean Defects

2021 ◽  
Vol 11 (17) ◽  
pp. 8226
Author(s):  
Shyang-Jye Chang ◽  
Chien-Yu Huang
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Learning Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Coffee Bean ◽  
Accuracy Rate ◽  
Detection Model ◽  
Proposed Model ◽  
Coffee Beans

The detection of coffee bean defects is the most crucial step prior to bean roasting. Existing defect detection methods used in the specialty coffee bean industry entail manual screening and sorting, require substantial human resources, and are not standardized. To solve these problems, this study developed a deep learning algorithm to detect defects in coffee beans. The results reveal that when the pooling layer was used to enhance features and reduce neural dimensionality, some of the coffee been features were lost or misclassified. Therefore, a novel dimensionality reduction method was adopted to increase the ability of feature extraction. The developed model also overcame the drawbacks of padding causing blurred image boundaries and the dead neurons causing impeding feature propagation. Images of eight types of coffee beans were used to train and test the proposed detection model. The proposed method was verified to reduce the bias when classifying defects in coffee beans. The detection accuracy rate of the proposed model was 95.2%. When the model was only used to detect the presence of defects, the accuracy rate increased to 100%. Thus, the proposed model is highly accurate in coffee bean defect detection in the classification of eight types of coffee beans.

scholarly journals Deep Learning for Detecting and Classifying Ocean Objects: Application of YoloV3 for Iceberg–Ship Discrimination

2020 ◽  
Vol 9 (12) ◽  
pp. 758
Author(s):  
Frederik Seerup Hass ◽  
Jamal Jokar Arsanjani
Keyword(s):  
Deep Learning ◽  
Oil Spills ◽  
Weather Conditions ◽  
Automatic Monitoring ◽  
Learning Model ◽  
Training Data ◽  
The Arctic ◽  
Detection Accuracy ◽  
Marine Traffic ◽  
Deep Learning Model

Synthetic aperture radar (SAR) plays a remarkable role in ocean surveillance, with capabilities of detecting oil spills, icebergs, and marine traffic both at daytime and at night, regardless of clouds and extreme weather conditions. The detection of ocean objects using SAR relies on well-established methods, mostly adaptive thresholding algorithms. In most waters, the dominant ocean objects are ships, whereas in arctic waters the vast majority of objects are icebergs drifting in the ocean and can be mistaken for ships in terms of navigation and ocean surveillance. Since these objects can look very much alike in SAR images, the determination of what objects actually are still relies on manual detection and human interpretation. With the increasing interest in the arctic regions for marine transportation, it is crucial to develop novel approaches for automatic monitoring of the traffic in these waters with satellite data. Hence, this study aims at proposing a deep learning model based on YoloV3 for discriminating icebergs and ships, which could be used for mapping ocean objects ahead of a journey. Using dual-polarization Sentinel-1 data, we pilot-tested our approach on a case study in Greenland. Our findings reveal that our approach is capable of training a deep learning model with reliable detection accuracy. Our methodical approach along with the choice of data and classifiers can be of great importance to climate change researchers, shipping industries and biodiversity analysts. The main difficulties were faced in the creation of training data in the Arctic waters and we concluded that future work must focus on issues regarding training data.

scholarly journals Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations

Animals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 3089
Author(s):  
Anil Bhujel ◽  
Elanchezhian Arulmozhi ◽  
Byeong-Eun Moon ◽  
Hyeon-Tae Kim
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Automatic Monitoring ◽  
Physical Activities ◽  
Detection Accuracy ◽  
Tracking Accuracy ◽  
Short Term ◽  
Health And Wellbeing ◽  
High Detection ◽  
Tracking Model

Pig behavior is an integral part of health and welfare management, as pigs usually reflect their inner emotions through behavior change. The livestock environment plays a key role in pigs’ health and wellbeing. A poor farm environment increases the toxic GHGs, which might deteriorate pigs’ health and welfare. In this study a computer-vision-based automatic monitoring and tracking model was proposed to detect pigs’ short-term physical activities in the compromised environment. The ventilators of the livestock barn were closed for an hour, three times in a day (07:00–08:00, 13:00–14:00, and 20:00–21:00) to create a compromised environment, which increases the GHGs level significantly. The corresponding pig activities were observed before, during, and after an hour of the treatment. Two widely used object detection models (YOLOv4 and Faster R-CNN) were trained and compared their performances in terms of pig localization and posture detection. The YOLOv4, which outperformed the Faster R-CNN model, was coupled with a Deep-SORT tracking algorithm to detect and track the pig activities. The results revealed that the pigs became more inactive with the increase in GHG concentration, reducing their standing and walking activities. Moreover, the pigs shortened their sternal-lying posture, increasing the lateral lying posture duration at higher GHG concentration. The high detection accuracy (mAP: 98.67%) and tracking accuracy (MOTA: 93.86% and MOTP: 82.41%) signify the models’ efficacy in the monitoring and tracking of pigs’ physical activities non-invasively.

scholarly journals Recognition of Thyroid Ultrasound Standard Plane Images Based on Residual Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Minghui Guo ◽  
Kangjian Wang ◽  
Shunlan Liu ◽  
Yongzhao Du ◽  
Peizhong Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ultrasound Examination ◽  
Network Models ◽  
Learning Method ◽  
Thyroid Ultrasound ◽  
Accuracy Rate ◽  
Residual Network ◽  
Neural Network Models ◽  
Average Accuracy ◽  
Missed Diagnosis

Ultrasound is one of the critical methods for diagnosis and treatment in thyroid examination. In clinical application, many reasons, such as large outpatient traffic, time-consuming training of sonographers, and uneven professional level of physicians, often cause irregularities during the ultrasonic examination, leading to misdiagnosis or missed diagnosis. In order to standardize the thyroid ultrasound examination process, this paper proposes using a deep learning method based on residual network to recognize the Thyroid Ultrasound Standard Plane (TUSP). At first, referring to multiple relevant guidelines, eight TUSP were determined with the advice of clinical ultrasound experts. A total of 5,500 TUSP images of 8 categories were collected with the approval and review of the Ethics Committee and the patient’s informed consent. Then, after desensitizing and filling the images, the 18-layer residual network model (ResNet-18) was trained for TUSP image recognition, and five-fold cross-validation was performed. Finally, through indicators like accuracy rate, we compared the recognition effect of other mainstream deep convolutional neural network models. Experimental results showed that ResNet-18 has the best recognition effect on TUSP images with an average accuracy rate of 91.07%. The average macro precision, average macro recall, and average macro F1-score are 91.39%, 91.34%, and 91.30%, respectively. It proves that the deep learning method based on residual network can effectively recognize TUSP images, which is expected to standardize clinical thyroid ultrasound examination and reduce misdiagnosis and missed diagnosis.

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