Efficient Laser Paint Removal Strategy Through the Fusion of Computer Vision and Image Processing

As a modern surface treatment method, laser paint removal technology has been widely used in automobile, aviation, electronics and other industries. However, the traditional laser paint removal strategy is often based on fixed parameters and modes, and may not achieve the best treatment effect for different types and different States of paint layers. In this article, the image processing method based on deep learning (DL) is applied to the optimization of laser paint removal strategy. By constructing DL model, the image information of paint layer is analyzed in real time, and the parameters and strategies of laser paint removal are adjusted accordingly, so as to achieve higher processing efficiency and better processing effect. The results show that the processing speed of convolutional neural network (CNN) is obviously faster than that of recurrent neural network (RNN), which is mainly due to CNN's unique structure design and weight sharing mechanism, as well as its easier parallel calculation. In terms of processing accuracy, CNN also shows better performance than RNN, which is mainly due to CNN's powerful feature detection ability and its advantages of less demand for data and difficult over-fitting in the training stage.