A Drone-Based Building Inspection System Using Software-Agents

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 737)

Abstract

Regular building inspections are a key means of identifying defects before getting worse or causing a building failure. As a tool for building condition inspections, Unmanned Aerial Vehicles (UAVs) or drones offer considerable potential allowing especially high-rise buildings to be visually assessed with economic and risk-related benefits. One of the critical problems encountered in automating the system is that the whole process involves a very complicated and significant amount of computational tasks, such as UAV control, localisation, image acquisition and abnormality analysis using machine learning techniques. Distributed software agents interact and collaborate each other in complicated systems and improve the reliability, availability and scalability. This research introduces a ubiquitous concept of software-agents to a drone-based building inspection system that is applied to crack-detection on concrete surfaces. The architecture and new features of the proposed system will be discussed.

Keywords

Unmanned aerial vehicle Software agents Distributed system Building inspection Deep learning 

References

  1. 1.
    Giri, P., Kharkovsky, S.: Detection of surface crack in concrete using measurement technique with laser displacement sensor. IEEE Trans. Instrum. Meas. 65, 1951–1953 (2016)CrossRefGoogle Scholar
  2. 2.
    Cheng, M.Y., Wu, Y.W.: Multi-agent-based data exchange platform for bridge disaster prevention: A case study in Taiwan. Nat. Hazards 69, 311–326 (2013)CrossRefGoogle Scholar
  3. 3.
    Paolucci, M., Sacile, R.: Agent-based Manufacturing and Control Systems: New Agile Manufacturing Solutions for Achieving Peak Performance, CRC Press (2016)Google Scholar
  4. 4.
    Mocanu, A., Bădică, C.: Scrutable multi-agent hazard rescue system. In: Intelligent Distributed Computing, IX PP. 37–47, Springer (2016)Google Scholar
  5. 5.
    Chanda, S., Bu, G., Guan, H., Jo, J., Pal, U., Loo, Y.C., Blumenstein, M.: Automatic bridge crack detection–a texture analysis-based approach. In: IAPR Workshop on Artificial Neural Networks in Pattern Recognition, pp. 193–203, Springer (2014)Google Scholar
  6. 6.
    Jo, J., Tsunoda, Y., Sullivan, T., Lennon, M., Jo, T., Chun, Y.S.: BINS: blackboard-based intelligent navigation system for multiple sensory data integration. In Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV) 1 (The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp)) (2013)Google Scholar
  7. 7.
    Zosso, D., Tran, G., Osher, S.: A unifying retinex model based on non-local differential operators. In: IS&T/SPIE Electronic Imaging 865702-865702-865716. International Society for Optics and Photonics (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Institute for Integrated and Intelligent SystemsGriffith UniversityNathanAustralia

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