End-to-end roughness measurement can be achieved through the self-extraction of grinding surface features, which can be achieved through deep learning. However, due to the grinding surface texture being random, the features are weak, the self-extracted grinding surface features of the same surface under different lighting environments are different, and the training data and the test data when the lighting environments are inconsistent with the recognition of the measurement of the precision of the lower. To tackle these problems, this paper proposes an adversarial domain self-adaptation (NMDANN) based visual measurement method for grinding surface roughness under variable illumination. An improved residual network is used as a generator to extract more effective metastable features, and multi-head attention is introduced into the domain discriminator to enhance its domain adaptive capability. The experimental results show that the method can achieve an average recognition precision of 96.9112% for different grades of roughness on the grinding surface under the changing light environment, which is 40.1360% higher than the ordinary classification model ResNet50 and 10.1626% higher than the DANN model with migration capability. It lays the foundation for the online visual measurement of roughness on the grinding surface under the variable light environment. This lays the foundation for the online visualization of grinding surface roughness measurement in variable light environments.

中文翻译：

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可变光照下基于对抗域自适应的磨削表面粗糙度等级识别

摘要深度学习可以实现磨削表面特征的自提取，从而实现端到端的粗糙度测量。但由于磨削表面纹理随机，特征较弱，同一表面在不同光照环境下自提取的磨削表面特征不同，光照环境下的训练数据和测试数据不一致。认可的测量精度较低。为了解决这些问题，本文提出了一种基于对抗域自适应（NMDANN）的可变照明下磨削表面粗糙度视觉测量方法。使用改进的残差网络作为生成器来提取更有效的亚稳态特征，并将多头注意力引入域鉴别器以增强其域自适应能力。实验结果表明，该方法能够识别变化光环境下磨削表面不同等级的粗糙度，为变光环境下磨削表面粗糙度在线视觉测量奠定了基础。