A Laplacian Image-Guided Semantic Segmentation Framework for Disc Brake Vibration Displacement Measurement

Measuring the vibration displacement of disc brakes is essential since aberrant vibration has a direct impact on braking function and driving safety. In this research, we offer a semantic segmentation-based noncontact vision measurement framework that overcomes the accuracy and adaptability constrai...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 15
Main Authors: Shen, Aiping, Liu, Chang, Wang, Sen, Zhu, Liying
Format: Journal Article
Language:English
Published: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Brakes
Braking
Data integration
Data mining
Disc brake
Disc brakes
Displacement measurement
Feature extraction
High speed cameras
Image segmentation
Measurement methods
Root-mean-square errors
rotating machinery
Semantic segmentation
semantic segmentation algorithm
Semantics
Sensors
Vehicle safety
vibration displacement measurement
Vibration measurement
Vibrations
Video data
visual sensing technology
Wavelet transforms
ISSN:0018-9456, 1557-9662
Online Access:Get full text
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Summary:Measuring the vibration displacement of disc brakes is essential since aberrant vibration has a direct impact on braking function and driving safety. In this research, we offer a semantic segmentation-based noncontact vision measurement framework that overcomes the accuracy and adaptability constraints of conventional sensors. Considering that the fine localization of disc brakes depends more on spatial details, we design a haar wavelet transform-based downsampling approach (HWTD), a global-local feature extraction residual structure (GLFE), and a Laplacian image-guided feature fusion network (LIFFN) to achieve effective local information fusion, extraction, and retention. Experiments on video data captured by a high-speed camera under two operating conditions, namely, smooth operation and actual braking, show that the proposed method, respectively, achieves an average root mean square error (RMSE) of 12.559 and <inline-formula> <tex-math notation="LaTeX">18.803~\mu </tex-math></inline-formula>m for the vibration displacement data measured under the two conditions and performs excellently in both the time and frequency domains compared to the existing visual measurement methods.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2025.3584147