Near-field analysis of the high-power laser facility using calculated methods and a residual convolutional neural network with attention mechanism.

• A calculated method in space domain is presented to analyze the 1ω near-field status of SG-Ⅱlaser facility in batch, which can guide the operation and maintenance of the facility. • A residual convolutional neural network with attention mechanism is presented to identify near-field distribution features in a timely manner. • The spatial attention module (SA) enables the model to have the higher accuracy. • Methods can solve the problem that manual observation cannot detect laser near-field anomalies in time for large-scale science engineering. High-power solid-state laser facilities for Inertial confinement fusion impose very stringent requirements on the near-field distribution during operation. We propose calculated methods in space domain, including Otsu, adaptive rotation, cropping and numerical calculation to automatically process high power-laser near-field images from a large number of different status. These methods are applied to detect anomalous status based on contrast and modulation. It can also provide the dataset for the deep learning model. For characteristics of the 1ω near-field distributions due to various perturbations, a residual neural network with an added spatial attention mechanism is used to identify and classify features of numerous near-field images without human recognition. The accuracy of the model is close to 93%, which is better than the common residual neural network and VGG16 model. The results of this paper can be used to monitor the operation status and provide abnormal warning in the complex engineering of large-scale high-power laser facility based on timely analysis of the near-field status. [ABSTRACT FROM AUTHOR]