Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Transfer Learning-Based Crack Detection by AutonomousUAVs
Date
2018-07-20
Author
Küçüksubaşı, Fatih
Sorguç, Arzu
Metadata
Show full item record
Item Usage Stats
227
views
0
downloads
Cite This
Unmanned Aerial Vehicles (UAVs) have recently shown great performance collecting visual data through autonomous exploration and mapping in building inspection. Yet, the number of studies is limited considering the post processing of the data and its integration with autonomous UAVs. These will enable huge steps onward into full automation of building inspection. In this regard, this work presents a decision making tool for revisiting tasks in visual building inspection by autonomous UAVs. The tool is an implementation of fine-tuning a pretrained Convolutional Neural Network (CNN) for surface crack detection. It offers an optional mechanism for task planning of revisiting pinpoint locations during inspection. It is integrated to a quadrotor UAV system that can autonomously navigate in GPS-denied environments. The UAV is equipped with onboard sensors and computers for autonomous localization, mapping and motion planning. The integrated system is tested through simulations and real-world experiments. The results show that the system achieves crack detection and autonomous navigation in GPS-denied environments for building inspection.
Subject Keywords
Unmanned aerial vehicle
,
Building inspection
,
Crack detection
,
Transfer learning
,
Autonomous navigation
URI
https://hdl.handle.net/11511/72692
https://www.iaarc.org/publications/2018_proceedings_of_the_35th_isarc/transfer_learning_based_crack_detection_by_autonomous_uavs.html
DOI
https://doi.org/10.22260/ISARC2018/0081
Conference Name
35 th International Symposium on Automation and Robotics in Construction (20 - 25 Temmuz 2018)
Collections
Department of Architecture, Conference / Seminar
Suggestions
OpenMETU
Core
Unmanned Aerial Vehicle Domain: Areas of Research
Demir, Kadir Alpaslan; Cicibas, Halil; ARICA, NAFİZ (2015-07-01)
Unmanned aerial vehicles (UAVs) domain has seen rapid developments in recent years. As the number of UAVs increases and as the missions involving UAVs vary, new research issues surface. An overview of the existing research areas in the UAV domain has been presented including the nature of the work categorised under different groups. These research areas are divided into two main streams: Technological and operational research areas. The research areas in technology are divided into onboard and ground techno...
Vision-Based Detection and Distance Estimation of Micro Unmanned Aerial Vehicles
Gökçe, Fatih; Üçoluk, Göktürk; Şahin, Erol; Kalkan, Sinan (MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2015-9)
Detection and distance estimation of micro unmanned aerial vehicles (mUAVs) is crucial for (i) the detection of intruder mUAVs in protected environments; (ii) sense and avoid purposes on mUAVs or on other aerial vehicles and (iii) multi-mUAV control scenarios, such as environmental monitoring, surveillance and exploration. In this article, we evaluate vision algorithms as alternatives for detection and distance estimation of mUAVs, since other sensing modalities entail certain limitations on the environment...
Nonlinear Guidance of Aircraft Formations
Tekinalp, Ozan (null; 2015-01-05)
Nonlinear formation flight control algorithm for a pair of unmanned aerial vehicles (UAV) is proposed. The leader-follower approach to formation flight is adopted. The leader maintains a prescribed trajectory while the follower is controlled to track and maintain a fixed relative distance from its leader. Two nonlinear guidance algorithms, Lyapunov and State Dependent Ricatti Equation, (SDRE) based are proposed for the relative guidance of the follower UAV. The resulting formation control systems are tested...
GPS based altitude control of an unmanned air vehicle using digital terrain elevation data
Ataç, Selçuk; Platin, Bülent Emre; Department of Mechanical Engineering (2006)
In this thesis, an unmanned air vehicle (UAV) is used to develop a prototype base test platform for flight testing of new control algorithms and avionics for advanced UAV system development applications. A control system that holds the UAV at a fixed altitude above the ground is designed and flight tested. Only the longitudinal motion of the UAV is considered during the controller design, hence its lateral motions are controlled manually by a remote control unit from the ground. UAV’s altitude with respect ...
Vision-aided landing for fixed wing unmanned aerial vehicle
Esin, Engin; Kutay, Ali Türker; Department of Aerospace Engineering (2016)
The aim of this thesis is to design an autoland system for fixed wing unmanned aerial vehicle (UAV) to make auto landing by using position information calculated by image processing algorithms. With this ability, even if GPS is not available to be used, UAV still could make a safe automatic landing. Landing autopilot is aimed to keep UAV on a straight line with a constant flight path angle. Therefore, landing autopilot and computer vision methods are studied within the scope of this thesis. Also, to test de...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
F. Küçüksubaşı and A. Sorguç, “Transfer Learning-Based Crack Detection by AutonomousUAVs,” presented at the 35 th International Symposium on Automation and Robotics in Construction (20 - 25 Temmuz 2018), Berlin, Germany, 2018, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/72692.