scholarly journals Health Assessment of Eucalyptus Trees Using Siamese Network from Google Street and Ground Truth Images

2021 ◽  
Vol 13 (11) ◽  
pp. 2194
Author(s):  
Asim Khan ◽  
Warda Asim ◽  
Anwaar Ulhaq ◽  
Bilal Ghazi ◽  
Randall W. Robinson
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Algorithm ◽  
Health Assessment ◽  
Ground Truth ◽  
Urban Ecosystem ◽  
City Council ◽  
Local Council ◽  
Deep Learning Algorithm ◽  
Urban Greenery

Urban greenery is an essential characteristic of the urban ecosystem, which offers various advantages, such as improved air quality, human health facilities, storm-water run-off control, carbon reduction, and an increase in property values. Therefore, identification and continuous monitoring of the vegetation (trees) is of vital importance for our urban lifestyle. This paper proposes a deep learning-based network, Siamese convolutional neural network (SCNN), combined with a modified brute-force-based line-of-bearing (LOB) algorithm that evaluates the health of Eucalyptus trees as healthy or unhealthy and identifies their geolocation in real time from Google Street View (GSV) and ground truth images. Our dataset represents Eucalyptus trees’ various details from multiple viewpoints, scales and different shapes to texture. The experiments were carried out in the Wyndham city council area in the state of Victoria, Australia. Our approach obtained an average accuracy of 93.2% in identifying healthy and unhealthy trees after training on around 4500 images and testing on 500 images. This study helps in identifying the Eucalyptus tree with health issues or dead trees in an automated way that can facilitate urban green management and assist the local council to make decisions about plantation and improvements in looking after trees. Overall, this study shows that even in a complex background, most healthy and unhealthy Eucalyptus trees can be detected by our deep learning algorithm in real time.

scholarly journals Real-Time 3D Surface Measurement in Additive Manufacturing Using Deep Learning

2021 ◽  
Author(s):  
Liu Chenang ◽  
Wang Rongxuan ◽  
Zhenyu Kong ◽  
Babu Suresh ◽  
Joslin Chase ◽  
...  
Keyword(s):  
Deep Learning ◽  
Additive Manufacturing ◽  
Surface Morphology ◽  
Real Time ◽  
Learning Algorithm ◽  
Surface Measurement ◽  
Monitoring And Control ◽  
Deep Learning Algorithm ◽  
3D Surface ◽  
3D Surface Morphology

Layer-wise 3D surface morphology information is critical for the quality monitoring and control of additive manufacturing (AM) processes. However, most of the existing 3D scan technologies are either contact or time consuming, which are not capable of obtaining the 3D surface morphology data in a real-time manner during the process. Therefore, the objective of this study is to achieve real-time 3D surface data acquisition in AM, which is achieved by a supervised deep learning-based image analysis approach. The key idea of this proposed method is to capture the correlation between 2D image and 3D point cloud, and then quantify this relationship by using a deep learning algorithm, namely, convolutional neural network (CNN). To validate the effectiveness and efficiency of the proposed method, both simulation and real-world case studies were performed. The results demonstrate that this method has strong potential to be applied for real-time surface morphology measurement in AM, as well as other advanced manufacturing processes.

scholarly journals Real Time LDR Data Prediction using IoT and Deep Learning Algorithm

2020 ◽  
pp. 158-161
Author(s):  
Chandraprabha S ◽  
Pradeepkumar G ◽  
Dineshkumar Ponnusamy ◽  
Saranya M D ◽  
Satheesh Kumar S ◽  
...  
Keyword(s):  
Deep Learning ◽  
Light Intensity ◽  
Real Time ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Time Interval ◽  
Deep Learning Algorithm ◽  
Data Prediction ◽  
Solar Plant

This paper outfits artificial intelligence based real time LDR data which is implemented in various applications like indoor lightning, and places where enormous amount of heat is produced, agriculture to increase the crop yield, Solar plant for solar irradiance Tracking. For forecasting the LDR information. The system uses a sensor that can measure the light intensity by means of LDR. The data acquired from sensors are posted in an Adafruit cloud for every two seconds time interval using Node MCU ESP8266 module. The data is also presented on adafruit dashboard for observing sensor variables. A Long short-term memory is used for setting up the deep learning. LSTM module uses the recorded historical data from adafruit cloud which is paired with Node MCU in order to obtain the real-time long-term time series sensor variables that is measured in terms of light intensity. Data is extracted from the cloud for processing the data analytics later the deep learning model is implemented in order to predict future light intensity values.

scholarly journals Indoor and Outdoor Fire Localization Using YOLO Algorithm

2021 ◽  
Vol 2114 (1) ◽  
pp. 012067
Author(s):  
Ruba R. Nori ◽  
Rabah N. Farhan ◽  
Safaa Hussein Abed
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Learning Algorithms ◽  
Fire Detection ◽  
Time Data ◽  
Deep Learning Algorithm ◽  
Positive Elements ◽  
Image Dataset

Abstract Novel algorithm for fire detection has been introduced. CNN based System localization of fire for real time applications was proposed. Deep learning algorithms shows excellent results in a way that it accuracy reaches very high accuracy for fire image dataset. Yolo is a superior deep learning algorithm that is capable of detect and localize fires in real time. The luck of image dataset force us to limit the system in binary classification test. Proposed model was tested on dataset gathered from the internet. In this article, we built an automated alert system integrating multiple sensors and state-of-the art deep learning algorithms, which have a limited number of false positive elements and which provide our prototype robot with reasonable accuracy in real-time data and as little as possible to track and record fire events as soon as possible.

Real-Time Road Monitoring Using Deep Learning Algorithm Deployed on IoT Devices

2021 ◽  
pp. 137-147
Author(s):  
Nilay Nishant ◽  
Ashish Maharjan ◽  
Dibyajyoti Chutia ◽  
P. L. N. Raju ◽  
Ashis Pradhan
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Iot Devices

Digital Twin For Offshore Pipeline Corrosion Monitoring: A Deep Learning Approach

2021 ◽  
Author(s):  
Subrata Bhowmik
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Processing Time ◽  
Learning Algorithm ◽  
Corrosion Monitoring ◽  
Digital Twin ◽  
Pipeline Inspection ◽  
Deep Learning Algorithm ◽  
Inspection Data ◽  
Pipeline Corrosion

Abstract Pipeline corrosion is a major identified threat in the offshore oil and gas industry. In this paper, a novel computer vision-based digital twin concept for real-time corrosion inspection is proposed. The Convolution Neural Network (CNN) algorithm is used for the automated corrosion identification and classification from the ROV images and In-Line Inspection data. Predictive and prescriptive maintenance strategies are recommended based on the corrosion assessment through the digital twin. A Deep-learning Image processing model is developed based on the pipeline inspection images and In-Line Inspection images from some previous inspection data sets. During the corrosion monitoring through pipeline inspection, the digital twin system would be able to gather data and, at the same time, process and analyze the collected data. The analyzed data can be used to classify the corrosion type and determine the actions to be taken (develop predictive and prescriptive maintenance strategy). Convolution Neural Network, a well known Deep Learning algorithm, is used in the Tensorflow framework with Keras in the backend is used in the digital twin for corrosion inspection. CNN algorithm will first detect the corrosion and then the type of corrosion based on image classification. The deep-learning network training is done using 4000 images taken from the inspection video frames from a subsea pipeline inspection using ROV. The performances of both the methods are compared based on result accuracy as well as processing time. Deep Learning algorithm, CNN has approximately 81% accuracy for correctly identifying the corrosion and classify them based on severity through image classification. The processing time for the deep-learning method is significantly faster, and the digital twin generates the predictive or prescriptive strategy based on the inspection result in real-time. Deep-learning based digital twin for Corrosion inspection significantly improve current corrosion identification and reduce the overall time for offshore inspection. The inspection data loss due to the communication interference during real-time assessment can be eliminated using the digital twin. The image data can recover the required features based on other features available through the previous inspection. Furthermore, the system can adapt to the unrefined environment, making the proposed system robust and useful for other detection applications. The digital twin makes a recommended decision based on an expert system database during the real-time inspection. The complete corrosion monitoring process is performed in real-time on a cloud-based digital twin. The proposed pipeline corrosion inspection digital twin based on the CNN method will significantly reduce the overall maintenance cost and improve the efficiency of the corrosion monitoring system.

scholarly journals Vision-Based Deep Learning Algorithm for Detecting Potholes

2021 ◽  
Author(s):  
Kanushka Gajjar ◽  
Theo van Niekerk ◽  
Thomas Wilm ◽  
Paolo Mercorelli
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Learning Algorithm ◽  
Detection System ◽  
Detection Methods ◽  
Deep Learning Algorithm ◽  
Learning Techniques ◽  
Pothole Detection ◽  
Adequate Data

Potholes on roads pose a major threat to motorists and autonomous vehicles. Driving over a pothole has the potential to cause serious damage to a vehicle, which in turn may result in fatal accidents. Currently, many pothole detection methods exist. However, these methods do not utilize deep learning techniques to detect a pothole in real-time, determine the location thereof and display its location on a map. The success of determining an effective pothole detection method, which includes the aforementioned deep learning techniques, is dependent on acquiring a large amount of data, including images of potholes. Once adequate data had been gathered, the images were processed and annotated. The next step was to determine which deep learning algorithms could be utilized. Three different models, including Faster R-CNN, SSD and YOLOv3 were trained on the custom dataset containing images of potholes to determine which network produces the best results for real-time detection. It was revealed that YOLOv3 produced the most accurate results and performed the best in real-time, with an average detection time of only 0.836s per image. The final results revealed that a real-time pothole detection system, integrated with a cloud and maps service, can be created to allow drivers to avoid potholes.

scholarly journals Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246472
Author(s):  
Eun Young Kim ◽  
Young Jae Kim ◽  
Won-Jun Choi ◽  
Gi Pyo Lee ◽  
Ye Ra Choi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Operating Characteristic ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Health Screening ◽  
Health Care Clinics ◽  
Radiologic Findings ◽  
Deep Learning Algorithm ◽  
Chest X Ray ◽  
Good Stand

Purpose This study evaluated the performance of a commercially available deep-learning algorithm (DLA) (Insight CXR, Lunit, Seoul, South Korea) for referable thoracic abnormalities on chest X-ray (CXR) using a consecutively collected multicenter health screening cohort. Methods and materials A consecutive health screening cohort of participants who underwent both CXR and chest computed tomography (CT) within 1 month was retrospectively collected from three institutions’ health care clinics (n = 5,887). Referable thoracic abnormalities were defined as any radiologic findings requiring further diagnostic evaluation or management, including DLA-target lesions of nodule/mass, consolidation, or pneumothorax. We evaluated the diagnostic performance of the DLA for referable thoracic abnormalities using the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity using ground truth based on chest CT (CT-GT). In addition, for CT-GT-positive cases, three independent radiologist readings were performed on CXR and clear visible (when more than two radiologists called) and visible (at least one radiologist called) abnormalities were defined as CXR-GTs (clear visible CXR-GT and visible CXR-GT, respectively) to evaluate the performance of the DLA. Results Among 5,887 subjects (4,329 males; mean age 54±11 years), referable thoracic abnormalities were found in 618 (10.5%) based on CT-GT. DLA-target lesions were observed in 223 (4.0%), nodule/mass in 202 (3.4%), consolidation in 31 (0.5%), pneumothorax in one 1 (<0.1%), and DLA-non-target lesions in 409 (6.9%). For referable thoracic abnormalities based on CT-GT, the DLA showed an AUC of 0.771 (95% confidence interval [CI], 0.751–0.791), a sensitivity of 69.6%, and a specificity of 74.0%. Based on CXR-GT, the prevalence of referable thoracic abnormalities decreased, with visible and clear visible abnormalities found in 405 (6.9%) and 227 (3.9%) cases, respectively. The performance of the DLA increased significantly when using CXR-GTs, with an AUC of 0.839 (95% CI, 0.829–0.848), a sensitivity of 82.7%, and s specificity of 73.2% based on visible CXR-GT and an AUC of 0.872 (95% CI, 0.863–0.880, P <0.001 for the AUC comparison of GT-CT vs. clear visible CXR-GT), a sensitivity of 83.3%, and a specificity of 78.8% based on clear visible CXR-GT. Conclusion The DLA provided fair-to-good stand-alone performance for the detection of referable thoracic abnormalities in a multicenter consecutive health screening cohort. The DLA showed varied performance according to the different methods of ground truth.

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