Subpixel Displacement Measurement at 784 FPS: From Algorithm to Hardware System
Measuring object displacement with subpixel-level accuracy is attracting increasing attention in numerous computer-vision-based applications, because of its high potential in compensating for camera resolution. Although ultrahigh-speed measurement is highly desired in many fields, existing researche...
Gespeichert in:
Veröffentlicht in: | IEEE transactions on instrumentation and measurement 2022, Vol.71, p.1-10 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Measuring object displacement with subpixel-level accuracy is attracting increasing attention in numerous computer-vision-based applications, because of its high potential in compensating for camera resolution. Although ultrahigh-speed measurement is highly desired in many fields, existing researches on subpixel displacement measurement mainly concentrate on accuracy, and few works attempt to reach high measurement speed. This article proposes an ultrahigh-speed subpixel displacement measurement approach. From algorithm perspective, a feature fusion-based directional sign-only correlation, a fitting-free subpixel displacement measurement method, and a dual-diagonals intensity-gap quantification are proposed. From hardware-system perspective, a system-level architecture for the proposed hardware-friendly algorithm is designed for highly-parallel processing. The proposed hardware architecture is implemented as a practical system by integrating a high-speed industrial camera, a field programmable gate array (FPGA), and a computer. Experimental results show that the proposed approach works in real-time at 784 frames per second (FPS) in image processing core with a delay of 0.75 ms per frame, while the average measurement stability quantified by the process capability index (PCI) improves 5.75 compared with discrete cosine transform (DCT) sign-only correlation. |
---|---|
ISSN: | 0018-9456 1557-9662 |
DOI: | 10.1109/TIM.2022.3162290 |