At present, machine vision roughness detection mostly needs to design roughness related indexes based on images, and the index design has human intervention and is heavily dependent on the light source environment. To solve this problem, the paper classifies the surface roughness based on the deep convolutional neural network method, which can realize the roughness detection without index design. The most important thing is that the detection method has good light source robustness under different light source environments. The study adopts an end-to-end image analysis method, by means of image enhancement pre-processing of a small number of source images, after multi-layer convolution and pooling operations, as well as comprehensive processing of fully connected and classification layers, the convolutional kernel can automatically extract the features of the image, and finally the surface roughness obtained by vertical disc cutter milling can be classified and predicted. In addition, based on the experimental light source environment with two different luminance of night and day, by comparing with the technically mature ResNet50 and DenseNet121 convolutional neural network models, the deep convolutional neural network Xception model not only has high roughness classification accuracy, but also has more light source environment robustness. This method makes the online measurement of visual roughness possible.

目前，机器视觉粗糙度检测大多需要根据图像设计粗糙度相关指标，且指标设计具有人为干预，严重依赖光源环境。针对这一问题，本文基于深度卷积神经网络方法对表面粗糙度进行分类，无需指标设计即可实现粗糙度检测。最重要的是该检测方法在不同光源环境下具有良好的光源鲁棒性。本研究采用端到端的图像分析方法，通过对少量源图像进行图像增强预处理，经过多层卷积和池化操作，以及全连接层和分类层的综合处理，卷积核可以自动提取图像特征，最终可以对立式盘刀铣削得到的表面粗糙度进行分类预测。此外，基于昼夜两种不同亮度的实验光源环境，通过与技术成熟的 ResNet50 和 DenseNet121 卷积神经网络模型进行对比，深度卷积神经网络 Xception 模型不仅具有较高的粗糙度分类精度，而且具有更强的光源环境鲁棒性。这种方法使视觉粗糙度的在线测量成为可能。基于昼夜两种不同亮度的实验光源环境，通过与技术成熟的ResNet50和DenseNet121卷积神经网络模型相比，深度卷积神经网络Xception模型不仅粗糙度分类精度高，而且具有更多的光照源环境健壮性。这种方法使视觉粗糙度的在线测量成为可能。基于昼夜两种不同亮度的实验光源环境，通过与技术成熟的ResNet50和DenseNet121卷积神经网络模型相比，深度卷积神经网络Xception模型不仅粗糙度分类精度高，而且具有更多的光照源环境健壮性。这种方法使视觉粗糙度的在线测量成为可能。