Accurate Strawberry Plant Detection System Based on Low-altitude Remote Sensing and Deep Learning Technologies

2020 ◽  
Author(s):  
Huazhe Zhang ◽  
Ping Lin ◽  
Jianqiang He ◽  
Yongming Chen
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Detection System ◽  
Learning Technologies ◽  
Strawberry Plant ◽  
Low Altitude

scholarly journals Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety

2020 ◽  
Vol 12 (1) ◽  
pp. 182 ◽  
Author(s):  
Lingxuan Meng ◽  
Zhixing Peng ◽  
Ji Zhou ◽  
Jirong Zhang ◽  
Zhenyu Lu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Detection System ◽  
Detection Model ◽  
Aerial Vehicle ◽  
Real Time Detection ◽  
Pipeline Safety

Unmanned aerial vehicle (UAV) remote sensing and deep learning provide a practical approach to object detection. However, most of the current approaches for processing UAV remote-sensing data cannot carry out object detection in real time for emergencies, such as firefighting. This study proposes a new approach for integrating UAV remote sensing and deep learning for the real-time detection of ground objects. Excavators, which usually threaten pipeline safety, are selected as the target object. A widely used deep-learning algorithm, namely You Only Look Once V3, is first used to train the excavator detection model on a workstation and then deployed on an embedded board that is carried by a UAV. The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy. Then, the UAV for an excavator detection system (UAV-ED) is further constructed for operational application. UAV-ED is composed of a UAV Control Module, a UAV Module, and a Warning Module. A UAV experiment with different scenarios was conducted to evaluate the performance of the UAV-ED. The whole process from the UAV observation of an excavator to the Warning Module (350 km away from the testing area) receiving the detection results only lasted about 1.15 s. Thus, the UAV-ED system has good performance and would benefit the management of pipeline safety.

scholarly journals Self-Organizing Deep Learning (SO-UNet)—A Novel Framework to Classify Urban and Peri-Urban Forests

2021 ◽  
Vol 13 (10) ◽  
pp. 5548
Author(s):  
Mohamad M. Awad ◽  
Marco Lauteri
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Spatial Resolution ◽  
Forest Type ◽  
Urban Forests ◽  
Learning Technologies ◽  
Aerial Images ◽  
Hyper Spectral ◽  
Type Classification ◽  
Self Organizing

Forest-type classification is a very complex and difficult subject. The complexity increases with urban and peri-urban forests because of the variety of features that exist in remote sensing images. The success of forest management that includes forest preservation depends strongly on the accuracy of forest-type classification. Several classification methods are used to map urban and peri-urban forests and to identify healthy and non-healthy ones. Some of these methods have shown success in the classification of forests where others failed. The successful methods used specific remote sensing data technology, such as hyper-spectral and very high spatial resolution (VHR) images. However, both VHR and hyper-spectral sensors are very expensive, and hyper-spectral sensors are not widely available on satellite platforms, unlike multi-spectral sensors. Moreover, aerial images are limited in use, very expensive, and hard to arrange and manage. To solve the aforementioned problems, an advanced method, self-organizing–deep learning (SO-UNet), was created to classify forests in the urban and peri-urban environment using multi-spectral, multi-temporal, and medium spatial resolution Sentinel-2 images. SO-UNet is a combination of two different machine learning technologies: artificial neural network unsupervised self-organizing maps and deep learning UNet. Many experiments have been conducted, and the results showed that SO-UNet overwhelms UNet significantly. The experiments encompassed different settings for the parameters that control the algorithms.

scholarly journals Landslides Information Extraction Using Object-Oriented Image Analysis Paradigm Based on Deep Learning and Transfer Learning

2020 ◽  
Vol 12 (5) ◽  
pp. 752 ◽  
Author(s):  
Heng Lu ◽  
Lei Ma ◽  
Xiao Fu ◽  
Chao Liu ◽  
Zhi Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Deep Learning ◽  
Transfer Learning ◽  
High Efficiency ◽  
Object Oriented ◽  
Histograms Of Oriented Gradients ◽  
Low Altitude ◽  
Uav Images ◽  
Landslide Disaster

How to acquire landslide disaster information quickly and accurately has become the focus and difficulty of disaster prevention and relief by remote sensing. Landslide disasters are generally featured by sudden occurrence, proposing high demand for emergency data acquisition. The low-altitude Unmanned Aerial Vehicle (UAV) remote sensing technology is widely applied to acquire landslide disaster data, due to its convenience, high efficiency, and ability to fly at low altitude under cloud. However, the spectrum information of UAV images is generally deficient and manual interpretation is difficult for meeting the need of quick acquisition of emergency data. Based on this, UAV images of high-occurrence areas of landslide disaster in Wenchuan County and Baoxing County in Sichuan Province, China were selected for research in the paper. Firstly, the acquired UAV images were pre-processed to generate orthoimages. Subsequently, multi-resolution segmentation was carried out to obtain image objects, and the barycenter of each object was calculated to generate a landslide sample database (including positive and negative samples) for deep learning. Next, four landslide feature models of deep learning and transfer learning, namely Histograms of Oriented Gradients (HOG), Bag of Visual Word (BOVW), Convolutional Neural Network (CNN), and Transfer Learning (TL) were compared, and it was found that the TL model possesses the best feature extraction effect, so a landslide extraction method based on the TL model and object-oriented image analysis (TLOEL) was proposed; finally, the TLOEL method was compared with the object-oriented nearest neighbor classification (NNC) method. The research results show that the accuracy of the TLOEL method is higher than the NNC method, which can not only achieve the edge extraction of large landslides, but also detect and extract middle and small landslides accurately that are scatteredly distributed.

scholarly journals Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kangrok Oh ◽  
Hae Min Kang ◽  
Dawoon Leem ◽  
Hyungyu Lee ◽  
Kyoung Yul Seo ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Low Income ◽  
Detection System ◽  
Learning Technologies ◽  
Fundus Photography ◽  
Wide Field ◽  
Middle Income ◽  
Middle Income Countries ◽  
Automated Screening

AbstractVisually impaired and blind people due to diabetic retinopathy were 2.6 million in 2015 and estimated to be 3.2 million in 2020 globally. Though the incidence of diabetic retinopathy is expected to decrease for high-income countries, detection and treatment of it in the early stages are crucial for low-income and middle-income countries. Due to the recent advancement of deep learning technologies, researchers showed that automated screening and grading of diabetic retinopathy are efficient in saving time and workforce. However, most automatic systems utilize conventional fundus photography, despite ultra-wide-field fundus photography provides up to 82% of the retinal surface. In this study, we present a diabetic retinopathy detection system based on ultra-wide-field fundus photography and deep learning. In experiments, we show that the use of early treatment diabetic retinopathy study 7-standard field image extracted from ultra-wide-field fundus photography outperforms that of the optic disc and macula centered image in a statistical sense.

scholarly journals OBJECT DETECTION SYSTEM FOR THE BLIND WITH VOICEGUIDANCE

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Rajeshvaree Ravindra Karmarkar ◽  
Prof.V.N Honmane
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Object Detection ◽  
Visually Impaired ◽  
Detection System ◽  
Learning Model ◽  
Learning Technologies ◽  
Industrial Facilities ◽  
Guidance Technique ◽  
Deep Learning Model

—As object recognition technology has developed recently, various technologies have been applied to autonomousvehicles, robots, and industrial facilities. However, the benefits ofthese technologies are not reaching the visually impaired, who need it the most. This paper proposed an object detection system for the blind using deep learning technologies. Furthermore, a voice guidance technique is used to inform sight impaired persons as to the location of objects. The object recognition deep learning model utilizes the You Only Look Once(YOLO) algorithm and a voice announcement is synthesized using text-tospeech (TTS) to make it easier for the blind to get information about objects. Asa result, it implements an efficient object-detection system that helps the blind find objects in a specific space without help from others, and the system is analyzed through experiments to verify performance.

scholarly journals Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review

2019 ◽  
Vol 5 (5) ◽  
pp. 52 ◽  
Author(s):  
Alberto Signoroni ◽  
Mattia Savardi ◽  
Annalisa Baronio ◽  
Sergio Benini
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Learning Technologies ◽  
Learning Approaches ◽  
Hyperspectral Image Analysis ◽  
Critical Issues ◽  
Multidisciplinary Perspective ◽  
Application Fields

Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.

scholarly journals IoT Based Fire Accident Detection System with Deep Learning Intelligence

2021 ◽  
Vol 11 (1) ◽  
pp. 138-142
Author(s):  
Hitanshi Jain ◽  
◽  
Sai Teja Miyapuram ◽  
Sree Ranga Reddy ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Predictive Analytics ◽  
Detection System ◽  
Workplace Safety ◽  
Learning Technologies ◽  
Heat Index ◽  
Sensor Temperature ◽  
Accident Detection ◽  
Fire Accident

A fire accident can be caused by many hazards, such as a propane tank, a defective product, a vehicle crash, or poor workplace safety. Because accidents involving fire are often unexpected and sudden, there isn’t a standard legal process for dealing with them, other than filing a negligence or workers compensation claim. This project aims to detect and monitor Fire Accident incidents well in advance and alert the surroundings to minimize the losses. This is an integration of IoT and Deep Learning Technologies, where sensors are used to collect the relevant data under the supervision of a controller unit. The controller unit collects and sends this data to a cloud database, from where the data for the Deep Learning model is fetched. This data is then used for making some insights and further predictive analytics. From the insights, many variables were found to be one of the reasons for a fire accident to take place. We considered the information about variables like Flame sensor, Temperature, Heat Index, GPS coordinates, Smoke, Type of Gases, Date, and Time for feature set generation and fed the model to a deep neural network for making future predictions. Comparing to existing conventional methods, this proposed method is different in terms of integrating deep learning with IoT. This method of approach will predict the chance of accidents priorly by classification of data.

A Novel Approach for Vehicle Type Classification and Speed Prediction Using Deep Learning

2020 ◽  
Vol 17 (5) ◽  
pp. 2237-2242
Author(s):  
E. S. Madhan ◽  
S. Neelakandan ◽  
R. Annamalai
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation System ◽  
Detection System ◽  
Learning Technologies ◽  
Training Dataset ◽  
Automation System ◽  
Vehicle Type ◽  
Novel Approach ◽  
Vehicle Type Classification ◽  
Type Classification

In Vehicles automation system, Classification and speed detection has become an important research challenge in road safety and intelligent transportation system. Many systems like pattern recognition, image processing and machine learning technologies have overcome numerous hindrances to accomplish this goal. In this paper, we demonstrate a speed detection system and vehicle type classification founded on deep learning technique. Moreover, we built up Modular Neural Network (MNN) architecture, advancement algorithm and its parameters are acquired by training dataset. This integrated part of a system will enhance to finding in automation detection and traffic flow management system.

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