Optimization of Smartphone-Based Strain Measurement Algorithm Utilizing Arc-Support Line Segments

Smartphone-based strain monitoring of structural components is an emerging approach to structural health monitoring. However, the existing techniques suffer from limited accuracy and poor cross-device adaptability. This study aims to optimize the smartphone-based Micro Image Strain Sensing (MISS) me...

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Veröffentlicht in:Buildings (Basel) Jg. 15; H. 18; S. 3407
Hauptverfasser: Cui, Qiwen, Gou, Changfei, Lu, Shengan, Xie, Botao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.09.2025
Schlagworte:
Accuracy
Adaptability
Algorithms
Cameras
circle detection
Civil engineering
Color temperature
Crack propagation
Deformation
Distortion
Errors
image distortion analysis
Lighting
Measurement
micro image strain sensing
Noise measurement
Optimization
Segments
Sensors
Smart phones
smartphone
Smartphones
Stability
Strain gauges
Strain measurement
strain monitoring
Structural health monitoring
ISSN:2075-5309, 2075-5309
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Zusammenfassung:Smartphone-based strain monitoring of structural components is an emerging approach to structural health monitoring. However, the existing techniques suffer from limited accuracy and poor cross-device adaptability. This study aims to optimize the smartphone-based Micro Image Strain Sensing (MISS) method by replacing the traditional Connected Component Labeling (CCL) algorithm with the arc-support line segments (ASLS) algorithm, thereby significantly enhancing the stability and adaptability of circle detection in micro-images captured by diverse smartphones. Additionally, this study evaluates the impact of lighting conditions and lens distortion on the optimized MISS method. The experimental results demonstrate that the ASLS algorithm outperforms CCL in terms of recognition accuracy (maximum error of 0.94%) and cross-device adaptability, exhibiting greater robustness against color temperature and focal length variations. Under fluctuating lighting conditions, the strain measurement noise remains within ±0.5 με and with a maximum error of 7.0 με compared to LVDT measurements, indicating the strong adaptability of the optimized MISS method to external light changes. Barrel distortion in microscopic images induces a maximum pixel error of 5.66%, yet the final optimized MISS method achieves highly accurate strain measurements. The optimized MISS method significantly improves measurement stability and engineering applicability, enabling effective large-scale implementation for strain monitoring of civil infrastructure.
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ISSN:2075-5309
2075-5309
DOI:10.3390/buildings15183407