Ground Vehicle Detection and Classification by an Unmanned Aerial Vehicle

2015 ◽  
Author(s):  
Raphael Montanari ◽  
Daniel C. Tozadore ◽  
Eduardo S. Fraccaroli ◽  
Roseli A.F. Romero
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vehicle Detection ◽  
Ground Vehicle ◽  
Aerial Vehicle

scholarly journals Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”

2020 ◽  
Vol 12 (6) ◽  
pp. 940 ◽  
Author(s):  
Xiuliang Jin ◽  
Zhenhai Li ◽  
Clement Atzberger
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Crop Production ◽  
Unmanned Ground Vehicle ◽  
Special Issue ◽  
Climate Conditions ◽  
Ground Vehicle ◽  
Global Food Security ◽  
3D Data ◽  
Potential Applications ◽  
Aerial Vehicle

High-throughput crop phenotyping is harnessing the potential of genomic resources for the genetic improvement of crop production under changing climate conditions. As global food security is not yet assured, crop phenotyping has received increased attention during the past decade. This spectral issue (SI) collects 30 papers reporting research on estimation of crop phenotyping traits using unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV) imagery. Such platforms were previously not widely available. The special issue includes papers presenting recent advances in the field, with 22 UAV-based papers and 12 UGV-based articles. The special issue covers 16 RGB sensor papers, 11 papers on multi-spectral imagery, and further 4 papers on hyperspectral and 3D data acquisition systems. A total of 13 plants’ phenotyping traits, including morphological, structural, and biochemical traits are covered. Twenty different data processing and machine learning methods are presented. In this way, the special issue provides a good overview regarding potential applications of the platforms and sensors, to timely provide crop phenotyping traits in a cost-efficient and objective manner. With the fast development of sensors technology and image processing algorithms, we expect that the estimation of crop phenotyping traits supporting crop breeding scientists will gain even more attention in the future.

scholarly journals Robotic complex for inspection of the outer surface of the aircraft in its parking lot

2020 ◽  
Vol 12 (S) ◽  
pp. 21-31
Author(s):  
Boris S. ALESHIN ◽  
Alexander I. CHERNOMORSKY ◽  
Eduard D. KURIS ◽  
Konstantin S. LELKOV ◽  
Maxim V. IVAKIN
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Outer Surface ◽  
Unmanned Ground Vehicle ◽  
Metal Fatigue ◽  
Ground Vehicle ◽  
Extreme Wind ◽  
Parking Lot ◽  
Wind Exposure ◽  
Wind Actions ◽  
Aerial Vehicle

Through-flight inspection of the outer surface of the aircraft is necessary to identify possible damage to the surface of the aircraft caused by metal fatigue, lightning, birds collision, etc. The article discusses the method of robotic inspection of the outer surface of the aircraft in its open air parking area. The robotic complex (RC) consists of an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV) interconnected by a tether mechanism (TM). The algorithm of the RC functioning is presented. The main attention is paid to the formation of the TM control and the features of its work, ensuring the prevention of collisions of UAV with aircraft during extreme wind actions on UAV. The study of the most critical mode of the complex operation under extreme wind actions on an unmanned aerial vehicle is carried out. The results of modeling the typical process of the RC operation in an abnormal conditions of extreme wind exposure to UAV are presented.

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.

scholarly journals Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery

2019 ◽  
Vol 11 (14) ◽  
pp. 1708 ◽  
Author(s):  
Shuang Cao ◽  
Yongtao Yu ◽  
Haiyan Guan ◽  
Daifeng Peng ◽  
Wanqian Yan
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vehicle Detection ◽  
Location Estimation ◽  
Affine Function ◽  
Response Elimination ◽  
Object Matching ◽  
Function Transformation ◽  
Segmentation Strategy ◽  
Aerial Vehicle ◽  
Uav Images

Vehicle detection from remote sensing images plays a significant role in transportation related applications. However, the scale variations, orientation variations, illumination variations, and partial occlusions of vehicles, as well as the image qualities, bring great challenges for accurate vehicle detection. In this paper, we present an affine-function transformation-based object matching framework for vehicle detection from unmanned aerial vehicle (UAV) images. First, meaningful and non-redundant patches are generated through a superpixel segmentation strategy. Then, the affine-function transformation-based object matching framework is applied to a vehicle template and each of the patches for vehicle existence estimation. Finally, vehicles are detected and located after matching cost thresholding, vehicle location estimation, and multiple response elimination. Quantitative evaluations on two UAV image datasets show that the proposed method achieves an average completeness, correctness, quality, and F1-measure of 0.909, 0.969, 0.883, and 0.938, respectively. Comparative studies also demonstrate that the proposed method achieves compatible performance with the Faster R-CNN and outperforms the other eight existing methods in accurately detecting vehicles of various conditions.

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