Semantic Segmentation of Cabbage in the South Korea Highlands with Images by Unmanned Aerial Vehicles

Identifying agricultural fields that grow cabbage in the highlands of South Korea is critical for accurate crop yield estimation. Only grown for a limited time during the summer, highland cabbage accounts for a significant proportion of South Korea’s annual cabbage production. Thus, it has a profound effect on the formation of cabbage prices. Traditionally, labor-extensive and time-consuming field surveys are manually carried out to derive agricultural field maps of the highlands. Recently, high-resolution overhead images of the highlands have become readily available with the rapid development of unmanned aerial vehicles (UAV) and remote sensing technology. In addition, deep learning-based semantic segmentation models have quickly advanced by recent improvements in algorithms and computational resources. In this study, we propose a semantic segmentation framework based on state-of-the-art deep learning techniques to automate the process of identifying cabbage cultivation fields. We operated UAVs and collected 2010 multispectral images under different spatiotemporal conditions to measure how well semantic segmentation models generalize. Next, we manually labeled these images at a pixel-level to obtain ground truth labels for training. Our results demonstrate that our framework performs well in detecting cabbage fields not only in areas included in the training data but also in unseen areas not included in the training data. Moreover, we analyzed the effects of infrared wavelengths on the performance of identifying cabbage fields. Based on the results of our framework, we expect agricultural officials to reduce time and manpower when identifying information about highlands cabbage fields by replacing field surveys.

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A New Navigation System for Unmanned Aerial Vehicles in Global Positioning System-Denied Environments Based On Image Registration with Mutual Information and Deep Learning

Proceedings of the 2020 International Technical Meeting of The Institute of Navigation ◽

10.33012/2020.17203 ◽

2020 ◽

Author(s):

Cagla Sahin ◽

Imam Samil Yetik

Keyword(s):

Deep Learning ◽

Image Registration ◽

Mutual Information ◽

Unmanned Aerial Vehicles ◽

Global Positioning System ◽

Navigation System ◽

Positioning System ◽

Aerial Vehicles ◽

Global Positioning

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EyeHealer: A large-scale anterior eye segment dataset with eye structure and lesion annotations

Precision Clinical Medicine ◽

10.1093/pcmedi/pbab009 ◽

2021 ◽

Author(s):

Wenjia Cai ◽

Jie Xu ◽

Ke Wang ◽

Xiaohong Liu ◽

Wenqin Xu ◽

...

Keyword(s):

Deep Learning ◽

Large Scale ◽

Image Annotation ◽

Clinical Care ◽

Significant Proportion ◽

Anterior Segment ◽

Future Research ◽

Learning Models ◽

Anterior Eye Segment ◽

Segmentation Models

Abstract Anterior segment eye diseases account for a significant proportion of presentations to eye clinics worldwide, including diseases associated with corneal pathologies, anterior chamber abnormalities (e.g. blood or inflammation) and lens diseases. The construction of an automatic tool for the segmentation of anterior segment eye lesions will greatly improve the efficiency of clinical care. With research on artificial intelligence progressing in recent years, deep learning models have shown their superiority in image classification and segmentation. The training and evaluation of deep learning models should be based on a large amount of data annotated with expertise, however, such data are relatively scarce in the domain of medicine. Herein, the authors developed a new medical image annotation system, called EyeHealer. It is a large-scale anterior eye segment dataset with both eye structures and lesions annotated at the pixel level. Comprehensive experiments were conducted to verify its performance in disease classification and eye lesion segmentation. The results showed that semantic segmentation models outperformed medical segmentation models. This paper describes the establishment of the system for automated classification and segmentation tasks. The dataset will be made publicly available to encourage future research in this area.

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A Flow Analysis Using a Water Tunnel of an Innovative Unmanned Aerial Vehicle

Applied Sciences ◽

10.3390/app11135772 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5772

Author(s):

Dawid Lis ◽

Adam Januszko ◽

Tadeusz Dobrocinski

Keyword(s):

Unmanned Aerial Vehicles ◽

Unmanned Aerial Vehicle ◽

Rapid Development ◽

Lift Coefficient ◽

Flow Analysis ◽

Water Tunnel ◽

Dual Use ◽

Aerial Vehicles ◽

Aerial Vehicle ◽

Technical Construction

The purpose of this article is to present and discuss the results of a non-standard unnamed aerial vehicle construction with a constant cross-section square-shaped avionic profile. Based on the model’s in-air observed maneuverability, the research of avionic construction behavior was carried out in a water tunnel. The results show the model’s specific lift capabilities in comparison to classical avionic constructions. The characteristic results of the lift coefficient showed that the unmanned aerial vehicle presents favorable features than classic avionic constructions. The model was created with the prospect of using it in the future for dual-use purposes, where unmanned aerial vehicles are currently experiencing very rapid development. When creating the prototype, the focus was on low production cost, as well as convenience in operation. The development of this type of breakthrough avionic solution, which shows extraordinary maneuverability, may contribute to increasing the popularity and, above all, the availability of unmanned aerial vehicles for the largest possible group of recipients because of high avionic properties in relation to the technical construction complexity.

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A new nonlinear lifting line method for aerodynamic analysis and deep learning modeling of small unmanned aerial vehicles

International Journal of Micro Air Vehicles ◽

10.1177/17568293211016817 ◽

2021 ◽

Vol 13 ◽

pp. 175682932110168

Author(s):

Hasan Karali ◽

Gokhan Inalhan ◽

M Umut Demirezen ◽

M Adil Yukselen

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Deep Learning ◽

Unmanned Aerial Vehicles ◽

Aerial Vehicles ◽

Aerial Vehicle ◽

Computationally Intensive ◽

Lifting Line ◽

Lifting Line Method ◽

Nonlinear Aerodynamic

In this work, a computationally efficient and high-precision nonlinear aerodynamic configuration analysis method is presented for both design optimization and mathematical modeling of small unmanned aerial vehicles. First, we have developed a novel nonlinear lifting line method which (a) provides very good match for the pre- and post-stall aerodynamic behavior in comparison to experiments and computationally intensive tools, (b) generates these results in order of magnitudes less time in comparison to computationally intensive methods such as computational fluid dynamics. This method is further extended to a complete configuration analysis tool that incorporates the effects of basic fuselage geometries. Moreover, a deep learning based surrogate model is developed using data generated by the new aerodynamic tool that can characterize the nonlinear aerodynamic performance of unmanned aerial vehicles. The major novel feature of this model is that it can predict the aerodynamic properties of unmanned aerial vehicle configurations by using only geometric parameters without the need for any special input data or pre-process phase as needed by other computational aerodynamic analysis tools. The obtained black-box function can calculate the performance of an unmanned aerial vehicle over a wide angle of attack range on the order of milliseconds, whereas computational fluid dynamics solutions take several days/weeks in a similar computational environment. The aerodynamic model predictions show an almost 1-1 coincidence with the numerical data even for configurations with different airfoils that are not used in model training. The developed model provides a highly capable aerodynamic solver for design optimization studies as demonstrated through an illustrative profile design example.

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3DLEB-Net: Label-Efficient Deep Learning-Based Semantic Segmentation of Building Point Clouds at LoD3 Level

Applied Sciences ◽

10.3390/app11198996 ◽

2021 ◽

Vol 11 (19) ◽

pp. 8996

Author(s):

Yuwei Cao ◽

Marco Scaioni

Keyword(s):

Deep Learning ◽

Semantic Segmentation ◽

Point Clouds ◽

Training Data ◽

Second Step ◽

Point Cloud Data ◽

Dynamic Graph ◽

Cloud Data ◽

Supervised Methods ◽

Global And Local

In current research, fully supervised Deep Learning (DL) techniques are employed to train a segmentation network to be applied to point clouds of buildings. However, training such networks requires large amounts of fine-labeled buildings’ point-cloud data, presenting a major challenge in practice because they are difficult to obtain. Consequently, the application of fully supervised DL for semantic segmentation of buildings’ point clouds at LoD3 level is severely limited. In order to reduce the number of required annotated labels, we proposed a novel label-efficient DL network that obtains per-point semantic labels of LoD3 buildings’ point clouds with limited supervision, named 3DLEB-Net. In general, it consists of two steps. The first step (Autoencoder, AE) is composed of a Dynamic Graph Convolutional Neural Network (DGCNN) encoder and a folding-based decoder. It is designed to extract discriminative global and local features from input point clouds by faithfully reconstructing them without any label. The second step is the semantic segmentation network. By supplying a small amount of task-specific supervision, a segmentation network is proposed for semantically segmenting the encoded features acquired from the pre-trained AE. Experimentally, we evaluated our approach based on the Architectural Cultural Heritage (ArCH) dataset. Compared to the fully supervised DL methods, we found that our model achieved state-of-the-art results on the unseen scenes, with only 10% of labeled training data from fully supervised methods as input. Moreover, we conducted a series of ablation studies to show the effectiveness of the design choices of our model.

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Super-Resolution Based on Generative Adversarial Network for HRTEM Images

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421540276 ◽

2021 ◽

pp. 2154027

Author(s):

Fuqi Mao ◽

Xiaohan Guan ◽

Ruoyu Wang ◽

Wen Yue

Keyword(s):

Deep Learning ◽

High Resolution ◽

Information Structure ◽

Rapid Development ◽

Super Resolution ◽

Training Data ◽

Learning Technology ◽

Hrtem Image ◽

Generative Adversarial Network ◽

Dual Regression

As an important tool to study the microstructure and properties of materials, High Resolution Transmission Electron Microscope (HRTEM) images can obtain the lattice fringe image (reflecting the crystal plane spacing information), structure image and individual atom image (which reflects the configuration of atoms or atomic groups in crystal structure). Despite the rapid development of HTTEM devices, HRTEM images still have limited achievable resolution for human visual system. With the rapid development of deep learning technology in recent years, researchers are actively exploring the Super-resolution (SR) model based on deep learning, and the model has reached the current best level in various SR benchmarks. Using SR to reconstruct high-resolution HRTEM image is helpful to the material science research. However, there is one core issue that has not been resolved: most of these super-resolution methods require the training data to exist in pairs. In actual scenarios, especially for HRTEM images, there are no corresponding HR images. To reconstruct high quality HRTEM image, a novel Super-Resolution architecture for HRTEM images is proposed in this paper. Borrowing the idea from Dual Regression Networks (DRN), we introduce an additional dual regression structure to ESRGAN, by training the model with unpaired HRTEM images and paired nature images. Results of extensive benchmark experiments demonstrate that the proposed method achieves better performance than the most resent SISR methods with both quantitative and visual results.

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A Method for Estimating the Area of Damaged Croplands and Woodlands with the Use of Unmanned Aerial Vehicles (Drones)

Proccedings of 10th International Conference "Environmental Engineering" ◽

10.3846/enviro.2017.227 ◽

2017 ◽

Author(s):

Tomasz Podciborski ◽

Jacek Kil

Keyword(s):

Natural Disasters ◽

Unmanned Aerial Vehicles ◽

Spatial Information ◽

Rapid Development ◽

Spatial Development ◽

Measurement Methods ◽

Loss Assessment ◽

Aerial Vehicles ◽

Extent Of Damage

Growing social demand for access to spatial information spurs the rapid development of measurement methods and systems for registering the results of spatial evaluations and analyses (Kwietniewski 2008). Any assessment of spatial development is carried out on the basis of information obtained from specific sources (Kowalczyk 2007). The main objective of this study was to propose a method for assessing the extent of damage caused by natural disasters to croplands and woodlands with the use of unmanned aerial vehicles (drones). The main aim was achieved through detailed goals, including determination of the causes of natural disasters, description of the field inspection procedure and development of loss assessment principles. The proposed method was verified in selected research sites, and the resulting damage report detailing cropland losses is presented in the study.

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