# Research on the Grinding Quality Evaluation of Composite Materials Based on Multi-Scale Texture Fusion Analysis

## TL;DR

This paper introduces a machine vision algorithm to evaluate the grinding quality of composite materials using multi-scale texture fusion, improving accuracy and efficiency over manual methods.

## Contribution

The novel integration of luminance analysis and multi-scale texture features through decision-level fusion improves automated surface quality evaluation.

## Key findings

- A modified Rayleigh parameter enables rapid pre-segmentation of unpolished areas based on surface reflection properties.
- The enhanced Otsu algorithm incorporating global grayscale mean and standard deviation improves defect detection accuracy.
- The proposed algorithm achieves 96% recognition accuracy and >95% reliability for surface grinding quality assessment.

## Abstract

To address the challenges of manual inspection dependency, low efficiency, and high costs in evaluating the surface grinding quality of composite materials, this study investigated machine vision-based surface recognition algorithms. We proposed a multi-scale texture fusion analysis algorithm that innovatively integrated luminance analysis with multi-scale texture features through decision-level fusion. Specifically, a modified Rayleigh parameter was developed during luminance analysis to rapidly pre-segment unpolished areas by characterizing surface reflection properties. Furthermore, we enhanced the traditional Otsu algorithm by incorporating global grayscale mean (μ) and standard deviation (σ), overcoming its inherent limitations of exclusive reliance on grayscale histograms and lack of multimodal feature integration. This optimization enables simultaneous detection of specular reflection defects and texture uniformity variations. To improve detection window adaptability across heterogeneous surface regions, we designed a multi-scale texture analysis framework operating at multiple resolutions. Through decision-level fusion of luminance analysis and multi-scale texture evaluation, the proposed algorithm achieved 96% recognition accuracy with >95% reliability, demonstrating robust performance for automated surface grinding quality assessment of composite materials.