激光清洗为工业清洗提供了优选方案，然而激光清洗机制是高度非线性的物理过程，造成激光清洗的检测困难。试验通过工艺分析与视觉图像分析激光除漆过程，建立了完备标准化的激光除漆图像数据集，采用卷积神经网络框架，优化深度残差网络以适用于除漆多类别分类的检测任务，在测试样本判别上实现了98.75%的准确率。证明了卷积神经网络在除漆判别任务上的泛用性，具有潜在的研究意义与实用价值。

Laser cleaning provides an optimal solution for industrial cleaning. However, the laser cleaning mechanism is a highly nonlinear physical process, which makes the detection of laser cleaning difficult. Through the process analysis and visual image analysis of laser paint removal process, a complete and standardized laser paint removal image data set is established. Convolution neural network framework is used to optimize the depth residual network, which is suitable for multi class paint removal detection tasks. The accuracy of 98.75% is achieved in the identification of test samples. It is proved that the deep residual network is widely used in the task of paint removal, and it has potential research significance and practical value.