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 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 $18.803~\mu $ 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.