論 文 の 要 旨
題

目A Study on High-Speed-Vision-Based Vibration Spectrum Imaging（ 高速ビジョンに基づく振動スペクトルイメージングに関する研究 ）氏 名 島﨑 航平
Several studies that utilized vibration information for inspections in various fields have been
conducted in recent years. Two main methods have been used to detect vibration displacement:
contact measurement and non-contact measurement. In contact measurement, contact sensors
such as accelerometers are directly installed on the target objects to inspect conditions. It isnecessary to attach the sensor to the reference point on the structure when attaching a contactsensor. However, it is often difficult or impossible to access the structural points at which it isinstalled. In non-contact measurement, the vibration displacement of the object is directly measured
with sensors such as an eddy current sensor, an optical fiber sensor, a laser Doppler vibrometer, andan offline-based high-speed vision system. Beneficial vibration analysis of the time-varying datameasured by optical sensors in the frequency domain can then be conducted after remotely
measuring the vibrated conditions of the target objects. Especially in the case of engineering, it isessential to measure the vibration displacement. Besides, it is always necessary to monitor andanalyze the running condition of a machine during its operation. It is necessary to perform vibration
analysis and vibration design of mechanical structures to improve their vibration resistance.
Therefore, vibration detection with non-contact sensors is essential.
Real-time high-speed vision systems to capture high-speed phenomena that the human eye cannot
see have been developed. With the acceleration of parallel processing on field programmable gatearrays (FPGAs) and graphics processing units (GPUs), various real-time application examples withhigh frame rates have been reported, such as bridge vibration analysis, vibration source localization,and multiple objects tracking systems. Such high-speed vision systems can function as dynamic
sensing tools for simultaneously analyzing rapid phenomena vibrating at frequencies of dozens or
hundreds of Hertz, which cannot be seen by the human eye and standard video signal formats (e.g,
NTSC 30 fps or PAL 25 fps).

In this study, we develop a vibration spectrum imaging system that implements the pixel-levelvibration frequency analysis to capture the time-varying brightness changes in images as avibration distribution. It realizes dynamic sensing functions for simultaneously analyzing fast
phenomena vibrating at frequencies of dozens or hundreds of Hz, which cannot be seen by thehuman eye. Our high-speed vision-based vibration analysis target is a human-invisible but
audible phenomenon vibrating at an audio frequency level. The vibration spectrum imaging
system consists of a high-speed vision used as a sensor for capturing the time-varying changes
in an image as a vibration distribution with the acceleration of parallel processing on GPU. We
propose novel concepts to realize that a vibration spectrum imaging for the high frequency
targets fluctuate at dozens or hundreds of Hz and high-speed moving and vibrating objects
such as multi-copter and honeybee. The pixel-level vibration spectrum imaging method expands
two types of applications for flying objects.

The first application proposes a fast algorithm for vision-based vibration source localization forflying multi-copter that can detect vibration sources at hundreds of Hertz by inspecting timevarying brightness signals at each pixel in HFR images. Our algorithm can significantly reducethe computational complexity of pixel-level digital filters for vibration source localization by
virtually adjusting the sampling rate to twice the vibration frequency of a target object to be
tracked using downsampled HFR images with frame interpolation while locking in vibration
frequency for tracking objects.
The second application is pixel-level flight activity sensing method by utilizing pixel-levelvibration source localization and vibration spectrum imaging for flying honeybee. This concept
shows the flight activity sensing that can track and quantify the vibration properties inbrightness at the pixel around wing-flapping honeybees by executing two-step pixel-level STFTs
for an HFR video. In several experimental results, flying honeybees in 1024×1024 images
captured at 500 fps demonstrate that, even when the image region of the honeybee is low
resolution, robust tracking can be achieved with activity sensing in the frequency range up to250 Hz, which is the Nyquist frequency when we analyze a 500 fps video. The trajectory of adetected and labeled honeybee is monitored with the peak frequency of these tracked STFTs as
its flight activity.
This thesis is organized as 6 Chapters.
Chapter 2 summarized related works on vibration measurement, high-speed vision and vibration
source tracking.
In Chapter 3, a real-time high-speed vision-based vibration spectrum imaging for visualizationof time-varying frequency response was developed to verify the effectiveness of our real-time
vibration spectrum imaging method. They were demonstrated using the vibration results for afan, multi-copter, and guitar string, which had rotating or vibrating frequencies in the range ofhundreds of Hz.
Chapter 4 explains the fast vibration source localization for a multi-copter using pixel-level
digital filters was proposed to verify the effectiveness of pixel-level accuracy in vibration source
tracking through several experimental results of offline high-framerate videos and real-time
implementation, which displayed various flight scenarios of a multi-copter with its propellers
rotating at 90-100 Hz.
In Chapter 5, an HFR-video-based honeybee activity sensing was proposed to realize theactivity sensing of honeybees flying in a natural outdoor environment by inspecting pixel-leveltemporal frequency responses in the brightness in an HFR video, computed by executing STFTsof the brightness signals at all pixels. The effectiveness of the proposed algorithm was verified
in several experiments which are how the spatial resolution of an HFR video affects thesensitivity in our honeybee activity sensing algorithm and the trajectories and flying activities ofhoneybees in the outdoor scene.
In Chapter 6, it summarized the contributions of this study and discussed future work.
The appendix included the summary and future implementation of Onsite-Team-Project in theTAOYAKA program which I belonged to become a global leader with a broad perspective andlearned onsite reverse innovation and bottom-up approach in a disadvantaged area with plentyof natural resources during master and Ph.D. courses in Hiroshima University.