scholarly journals A Waterway Monitoring Method of Unmanned Surface Vehicle Based on Deep Learning

2019 ◽  
Author(s):  
Yiheng Wu ◽  
Xing Li ◽  
Zhangjie Yin ◽  
Jian Li ◽  
Yan Zhou
Keyword(s):  
Deep Learning ◽  
Monitoring Method ◽  
Unmanned Surface Vehicle

scholarly journals A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone

Drones ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Apostolos Papakonstantinou ◽  
Marios Batsaris ◽  
Spyros Spondylidis ◽  
Konstantinos Topouzelis
Keyword(s):  
Deep Learning ◽  
Data Acquisition ◽  
Citizen Science ◽  
Coastal Zone ◽  
Automatic Detection ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Marine Litter ◽  
Density Maps ◽  
Learning Techniques

Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps.

scholarly journals Mapping Seasonal Agricultural Land Use Types Using Deep Learning on Sentinel-2 Image Time Series

2021 ◽  
Vol 13 (2) ◽  
pp. 289
Author(s):  
Misganu Debella-Gilo ◽  
Arnt Kristian Gjertsen
Keyword(s):  
Land Use ◽  
Time Series ◽  
Deep Learning ◽  
Agricultural Land ◽  
Satellite Image ◽  
Agricultural Land Use ◽  
Monitoring Method ◽  
Multiple Observations ◽  
Land Use Types ◽  
Sentinel 2

The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas.

scholarly journals Monitoring Method of Transmission Line Breaking Prevention Based on Deep Learning

2021 ◽  
Vol 252 ◽  
pp. 01024
Author(s):  
Jiang Yan ◽  
Li Qiang ◽  
Wang Guanyao ◽  
Wang Ben ◽  
Deng Wei
Keyword(s):  
Deep Learning ◽  
Transmission Line ◽  
Transmission Lines ◽  
Rapid Development ◽  
Video Data ◽  
Data Sets ◽  
Monitoring Method ◽  
Monitoring Model ◽  
Public Data ◽  
3D Cnn

With the rapid development of the national economy, the national power consumption level continues to increase, which puts forward higher requirements on the power supply guarantee capacity of the power grid system. The distribution range of the transmission line is wide and densely, most lines are exposed to the unguarded field without any shielding or protective measures, which are vulnerable to man-made destruction or natural disasters. Therefore, it is very important for the early monitoring and prevention of the external force breaking of the transmission lines. The method for preventing external breakage of transmission lines based on deep learning proposed in this paper utilizes the video data collected by the cameras erected on the transmission line roads to perform feature extraction and learning through 3D CNN and LSTM networks, and obtains a monitoring model for external breakage prevention of transmission lines. The model was tested on public data sets and verified that it has a good performance in the field of transmission lines against external damage. The method in this paper makes full use of the existing video acquisition equipment, and the process does not require human intervention, which greatly reduces the cost of line monitoring and the hidden dangers of accidents.

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