Microscopic bubbles segmentation of porcelain relics based on improved UNet++

The segmentation of microscopic bubbles of porcelain relics can provide a clearer observation of the morphology, quantity, and distribution of micro bubbles on the surface of porcelain, which is of great significance in assisting relic experts in classifying porcelain cultural relic fragments and identifying porcelain cultural relics. However, the bubbles in porcelain microscopic images are complex and varied, with uneven size and distribution. Existing image segmentation methods are difficult to adapt to the characteristics of porcelain microscopic bubbles.Therefore, a network named AGUNet++ based on convolution attention unit is proposed. This network utilizes a zigzag connection approach between nodes to fully extract image semantic features and prevent information loss. Meanwhile, a convolution attention unit is introduced by combining the dense skip connection of the convolution unit with the attention gate. The CAU enhances the learning of bubble regions relevant to the task of microscopic bubble segmentation in porcelain artifacts while suppressing irrelevant regions. Deep supervision and cross entropy loss are applied to the output of each sub network layer during the training process, which effectively enhance the ability to extract microscopic bubble features in porcelain artifacts and refine the segmentation results. The experimental results of this method on the SD-saliency-900 and PRMI demonstrate that AGUNet++exhibits certain improvements in MIoU, Precision, Recall, and F1_score, showing better segmentation performance compared to classical image segmentation networks.