This paper proposes an efficient methodology for predicting surface roughness using different soft computing approaches. The soft computing approaches are artificial neural network, adaptive neuro-fuzzy inference system and genetic algorithm. The proposed surface roughness prediction procedure has the following stages as feature extraction from the materials, classifications using random forests, adaptive neuro-fuzzy inference system (ANFIS). In this paper, the statistical features are extracted from material images as skewness, kurtosis, mean, variance, contrast, and energy.The surface roughness accuracy value varied between ANFIS and random forest classification in every measurement sequence. This limitation can be overcome by the genetic algorithm to optimize the best results. The optimization technique can produce more accurate surface roughness results for more than 98% and reduce the error rate up to 0.5%.

中文翻译：

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使用软计算方法进行表面粗糙度的计算机视觉测量和优化

本文提出了一种使用不同软计算方法预测表面粗糙度的有效方法。软计算方法有人工神经网络、自适应神经模糊推理系统和遗传算法。所提出的表面粗糙度预测程序具有以下阶段：从材料中提取特征、使用随机森林进行分类、自适应神经模糊推理系统（ANFIS）。本文从材料图像中提取统计特征，如偏度、峰度、均值、方差、对比度和能量。在每个测量序列中，ANFIS和随机森林分类的​​表面粗糙度精度值不同。遗传算法可以克服这种限制以优化最佳结果。