基于改进UCTransNet的海洋微藻图像分割模型

A Segmentation Model for Marine Microalgae Images Based on Improved UCTransNet

海洋微藻是海洋生态系统的基石,对其进行分割识别可以监测海洋水质并防治藻华。UCTransNet是使用Transformer模块替代UNet中跳跃连接模块的分割模型,但UCTransNet过于重视图像的通道信息而忽略了图像的空间信息。针对此情况,提出一种将空间与通道融合的注意力机制,并将其加入UCTransNet中,得到CSAM-UCTransNet。该模型加强了编码器与译码器之间的联系。实验表明,CSAM-UCTransNet对海洋微藻样本的分割精度相较于UCTransNet提升了4.88%。与UNet、Attention-UNet、UNet++等分割算法相比,该模型分割精度更高,对细节的处理效果更好。

Marine microalgae are the cornerstone of marine ecosystems,and their segmentation and identification can monitor marine water quality and prevent algal blooms.UCTransNet is a segmentation model that replaces the skip connection module in UNet with the Transformer module.However,UCTransNet places so much emphasis on the channel information of images that it ignores the spatial information of images.Aiming at this problem,this paper proposes a Channel-Space Attention Module(CSAM),and it is added to UCTransNet to obtain CSAM-UCTransNet.This model strengthens the connection between the encoder and decoder.The experiment shows that CSAM-UCTransNet improves the segmentation accuracy of marine microalgae samples by 4.88%compared to UCTransNet.Compared with other segmentation algorithms,such as UNet,Attention-UNet and UNet++,the proposed model has higher segmentation accuracy and better handling of details.