A point cloud semantic segmentation method for nuclear power reactors based on RandLA-Net Model

The detection of foreign objects in nuclear power plant reactor is a key task in the operation and maintenance of nuclear power plants. Loose and falling foreign objects such as bolts can lead to fuel component damage and unplanned shutdown, posing serious hazards. Therefore, we propose a point cloud semantic segmentation method for foreign objects in nuclear power plant reactor based on the RandLA-Net model. Considering the correlation between point cloud collection error and curvature, the data augmentation method is improved to reduce the risk of model overfitting. By treating the boundary points of different classes as the hard examples, the hard example mining is designed to improve model generalization performance. Adding an improved test time augmentation method during model inference, the more reliable segmentation results are performed by multiple prediction on points. The experimental results indicate that the proposed method can achieve high-accuracy reactor point cloud semantic segmentation with mIoU of 0.992 and mAcc of 0.997.