A FIELD SURVEY OF AIR-TO-GROUND TARGET-DETECTION PROBLEMS

1966 ◽  
Author(s):  
Samuel A. Hicks ◽  
Calvin G. Moler
Keyword(s):  
Target Detection ◽  
Field Survey ◽  
Ground Target

GROUND TARGET DETECTION AND TRACKING

2001 ◽  
pp. 171-176
Author(s):  
PHILIPPE LACOMME ◽  
JEAN-PHILIPPE HARDANGE ◽  
JEAN-CLAUDE MARCHAIS ◽  
ERIC NORMANT
Keyword(s):  
Target Detection ◽  
Detection And Tracking ◽  
Ground Target

Air-to-Ground Target Detection Algorithm Based on DenseNet and Channel Attention Mechanism

2020 ◽  
Vol 57 (22) ◽  
pp. 221010
Author(s):  
王文庆 Wang Wenqing ◽  
丰林 Feng Lin ◽  
刘洋 Liu Yang ◽  
杨东方 Yang Dongfang ◽  
张萌 Zhang Meng
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Attention Mechanism ◽  
Ground Target

scholarly journals A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 272 ◽  
Author(s):  
Ajmal Hinas ◽  
Roshan Ragel ◽  
Jonathan Roberts ◽  
Felipe Gonzalez
Keyword(s):  
Target Detection ◽  
Low Cost ◽  
Target Position ◽  
Position Estimation ◽  
Quality Data ◽  
Unmanned Aerial Systems ◽  
Modular Software ◽  
Ground Target ◽  
High Level ◽  
Aerial Systems

Small unmanned aerial systems (UASs) now have advanced waypoint-based navigation capabilities, which enable them to collect surveillance, wildlife ecology and air quality data in new ways. The ability to remotely sense and find a set of targets and descend and hover close to each target for an action is desirable in many applications, including inspection, search and rescue and spot spraying in agriculture. This paper proposes a robust framework for vision-based ground target finding and action using the high-level decision-making approach of Observe, Orient, Decide and Act (OODA). The proposed framework was implemented as a modular software system using the robotic operating system (ROS). The framework can be effectively deployed in different applications where single or multiple target detection and action is needed. The accuracy and precision of camera-based target position estimation from a low-cost UAS is not adequate for the task due to errors and uncertainties in low-cost sensors, sensor drift and target detection errors. External disturbances such as wind also pose further challenges. The implemented framework was tested using two different test cases. Overall, the results show that the proposed framework is robust to localization and target detection errors and able to perform the task.

Aerial surveillance based on hierarchical object classification for ground target detection

2015 ◽  
Author(s):  
Alberto Vázquez-Cervantes ◽  
Juan-Manuel García-Huerta ◽  
Teresa Hernández-Díaz ◽  
J. A. Soto-Cajiga ◽  
Hugo Jiménez-Hernández
Keyword(s):  
Target Detection ◽  
Object Classification ◽  
Aerial Surveillance ◽  
Ground Target
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