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Title |
Comparative Study on Feature Extraction Schemes for Feature-based Structural Displacement Measurement
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DOI |
https://doi.org/10.11112/jksmi.2024.28.3.74 |
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Keywords |
특징점 기반 변위 계측; 특징점 검출 알고리즘; 비전센서; 조도; 촬영거리 Feature-based displacement measurement; Feature detection algorithm; Vision sensor; Illuminance; Measurement distance |
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Abstract |
In this study, feature point detection and displacement measurement performance depending on feature extraction algorithms were compared and analyzed according to environmental changes and target types in the feature point-based displacement measurement algorithm. A three-story frame structure was designed for performance evaluation, and the displacement response of the structure was digitized into FHD (1920×1080) resolution. For performance analysis, the initial measurement distance was set to 10m, and increased up to 40m with an increment of 10m. During the experiments, illuminance was fixed to 450lux or 120lux. The artificial and natural targets mounted on the structure were set as regions of interest and used for feature point detection. Various feature detection algorithms were implemented for performance comparisons. As a result of the feature point detection performance analysis, the Shi-Tomasi corner and KAZE algorithm were found that they were robust to the target type, illuminance change, and increase in measurement distance. The displacement measurement accuracy using those two algorithms was also the highest. However, when using natural targets, the displacement measurement accuracy is lower than that of artificial targets. This indicated the limitation in extracting feature points as the resolution of the natural target decreased as the measurement distance increased.
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