Bandwidth Compression for Online Turbine Blade Defect Detection Scene via Optical Flow Method Based on Differential Estimation

Optical flow, as a feature representation of optical patterns, has been extensively studied and applied in motion detection. However, its application in defect detection remains rarely explored. To address the reliance of machine vision–based automated defect detection on large labelled datasets, which poses significant challenges on the detection executed to specialised components or online inspection scenes, we propose an alternative way to apply optical flow estimation as a method to discriminate the varying rates of mode clusters. Assuming optical flow components are values of the probability density function of optical mode, we consider optical flow estimation transitions through which optical mode characteristics are conveyed. Thereby, the modulation theory of stationary random processes (SRPs) is introduced to describe the basic equation of optical flow estimation. We remodel the basic equation according to the communication principle and shorten the calculation time. To better utilise the limited bandwidth of pattern space and achieve accurate pattern feature recognition within it, we consider optical flow estimation as an image‐based communication system and find the correspondences between them. By doing so, quantisation schemes and pyramid construction are evaluated under convex optimisation of optical flow estimation, and experimental results confirm quantisation scheme is feasible of the two. A modified ideal quantisation scheme is suggested to perform robust optical flow estimation. Experimental results reveal quantisation provided by Charbonnier compresses the output bandwidth in a wide range of parameter settings and guarantees an optical flow method with higher accuracy for automated machine vision detection executed in an unprepared industrial scene.