Object Detection on Thermal Images for Unmanned Aerial Vehicles Using Domain Adaption Through Fine-Tuning

2021 ◽  
Author(s):  
Jonas Rauch ◽  
Christopher Doer ◽  
Gert F. Trommer
Keyword(s):  
Object Detection ◽  
Unmanned Aerial Vehicles ◽  
Fine Tuning ◽  
Thermal Images ◽  
Aerial Vehicles ◽  
Domain Adaption

scholarly journals Editorial for Special Issue “Remote Sensing for Target Object Detection and Identification”

2020 ◽  
Vol 12 (1) ◽  
pp. 196 ◽  
Author(s):  
Gemine Vivone ◽  
Paolo Addesso ◽  
Amanda Ziemann
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Unmanned Aerial Vehicles ◽  
Target Object ◽  
Special Issue ◽  
Remotely Sensed Data ◽  
Aerial Vehicles ◽  
Detection And Identification ◽  
Different Types ◽  
Identification Techniques

This special issue gathers fourteen papers focused on the application of a variety of target object detection and identification techniques for remotely-sensed data. These data are acquired by different types of sensors (both passive and active) and are located on various platforms, ranging from satellites to unmanned aerial vehicles. This editorial provides an overview of the contributed papers, briefly presenting the technologies and algorithms employed as well as the related applications.

Global Thresholding for Scene Understanding Towards Autonomous Drone Navigation

2019 ◽  
Vol 23 (5) ◽  
pp. 909-919
Author(s):  
Alvin Wai Chung Lee ◽  
◽  
Suet-Peng Yong ◽  
Junzo Watada
Keyword(s):  
Object Detection ◽  
Unmanned Aerial Vehicles ◽  
Autonomous Navigation ◽  
Processing Time ◽  
Scene Understanding ◽  
Regions Of Interest ◽  
Gaussian Filter ◽  
Aerial Vehicles ◽  
Global Thresholding

Unmanned aerial vehicles, more typically known as drones are flying aircrafts that do not have a pilot onboard. For drones to fly through an area without GPS signals, developing scene understanding algorithms to assist in autonomous navigation will be useful. In this paper, various thresholding algorithms are evaluated to enhance scene understanding in addition to object detection. Based on the results obtained, Gaussian filter global thresholding can segment regions of interest in the scene effectively and provide the least cost of processing time.

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