Vehicle detection methods from an unmanned aerial vehicle platform

2012 ◽  
Author(s):  
Yingqian Yang ◽  
Fuqiang Liu ◽  
Ping Wang ◽  
Pingting Luo ◽  
Xiaofeng Liu
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vehicle Detection ◽  
Detection Methods ◽  
Aerial Vehicle ◽  
Vehicle Platform

scholarly journals Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1651 ◽  
Author(s):  
Suk-Ju Hong ◽  
Yunhyeok Han ◽  
Sang-Yeon Kim ◽  
Ah-Yeong Lee ◽  
Ghiseok Kim
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Migratory Bird ◽  
Aerial Images ◽  
Aerial Photographs ◽  
Detection Methods ◽  
Single Shot ◽  
Convolutional Network ◽  
Aerial Vehicle ◽  
Bird Detection

Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast any possible spread of animal disease such as avian influenza. This study led to the construction of deep-learning-based object-detection models with the aid of aerial photographs collected by an unmanned aerial vehicle (UAV). The dataset containing the aerial photographs includes diverse images of birds in various bird habitats and in the vicinity of lakes and on farmland. In addition, aerial images of bird decoys are captured to achieve various bird patterns and more accurate bird information. Bird detection models such as Faster Region-based Convolutional Neural Network (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot MultiBox Detector (SSD), Retinanet, and You Only Look Once (YOLO) were created and the performance of all models was estimated by comparing their computing speed and average precision. The test results show Faster R-CNN to be the most accurate and YOLO to be the fastest among the models. The combined results demonstrate that the use of deep-learning-based detection methods in combination with UAV aerial imagery is fairly suitable for bird detection in various environments.

scholarly journals Land surface reflectance retrieval from optical hyperspectral data collected with an unmanned aerial vehicle platform

2019 ◽  
Vol 27 (5) ◽  
pp. 7174 ◽  
Author(s):  
Yao-Kai Liu ◽  
Chuan-Rong Li ◽  
Ling-Ling Ma ◽  
Yong-Gang Qian ◽  
Ning Wang ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Land Surface ◽  
Hyperspectral Data ◽  
Surface Reflectance ◽  
Aerial Vehicle ◽  
Vehicle Platform ◽  
Land Surface Reflectance

Acoustical unmanned aerial vehicle detection in indoor scenarios using logistic regression model

2020 ◽  
pp. 1351010X2091785
Author(s):  
Gino Iannace ◽  
Giuseppe Ciaburro ◽  
Amelia Trematerra
Keyword(s):  
Logistic Regression ◽  
Unmanned Aerial Vehicle ◽  
Indoor Environment ◽  
Vehicle Detection ◽  
Sound Level ◽  
Shopping Center ◽  
Model Based ◽  
Aerial Vehicle ◽  
Level Meter ◽  
Indoor Scenarios

In this study, the data obtained from the acoustic measurements were used to train a model based on logistic regression in order to detect a quadrotor’s vehicle in indoor environment. To simulate a real environment, we made sound recordings in a shopping center. The sounds related to two scenarios were recorded: only anthropic noise and anthropic noise with background music. Later, we reproduced these sounds in an indoor environment of the same size and characteristics as the shopping center. During the simulation test, a drone placed at different distances from the sound level meter was turned on at different speeds to identify their presence in complex acoustic scenarios. Subsequently, these measurements were used to implement a model based on logistic regression for the automatic detection of the unmanned aerial vehicle. Logistic regression is widely used in pattern recognition of the binary dependent variable. This model returns high value of accuracy (0.994), indicating a high number of correct detections. The results obtained in this study suggest the use of this tool for unmanned aerial vehicle detection applications.

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