为了解决熔覆层表面气孔识别技术中耗时且准确度不足的问题, 文章利用深度学习技术中的语义分割网络提出了基于U-net神经网络识别熔覆层表面气孔的2BNC-Unet神经网络。通过引入Batch Normalization层以及串联注意力机制(CBAM)合理部署在神经网络中, 选取交并比(IoU)与Dice系数作为网络的评价指标。研究结果表明: 在测试集中, 2BNC-Unet网络的交并比与Dice系数分别为86.96%、86.42%, 相比U-net神经网络分别提高了7.65%、4.73%。同时为了验证该网络的性能, 选用SegNet、2BNC-Unet与U-net神经网络进行对比实验, 结果表明2BNC-Unet的分割效果不仅优于SegNet和U-net网络, 而且熔覆层表面的气孔细节能够被完整地分割。在深度学习技术中2BNC-Unet的分割速度和准确度都有了显著地提高, 气孔的分割为熔覆层的性能分析提供了帮助。

In order to solve the problems of time-consuming processes and insufficient accuracy in the surface porosity recognition technology of the cladding layer, A 2BNC-Unet neural network based on the U-Net neural network is proposed. The goal is to identify pores on the cladding layer's surface using semantic segmentation in deep learning technology. By introducing the Batch Normalization layer and the Convolutional Block Attention Module (CBAM) into the neural network in a reasonable manner, the Intersection over Union (IoU) and Dice coefficient were selected as evaluation indicators for the network. The results show that, in the test set, the intersection over union and Dice coefficient of the 2BNC-Unet network are 86.96% and 86.42%, respectively, which are 7.65% and 4.73% higher than those of the U-Net neural network. Additionally, to verify the performance of the network, comparative experiments were conducted using SegNet, 2BNC-Unet, and U-Net neural networks. The results demonstrate that the segmentation effect of 2BNC-Unet is not only better than that of SegNet and U-Net networks but also capable of completely segmenting the pore details on the cladding layer's surface. In the realm of deep learning technology, the segmentation speed and accuracy of 2BNC-Unet have been significantly improved, providing assistance in the performance analysis of cladding layers through pore segmentation.