江汉大学学报（自然科学版）2026，Vol.54Issue(4)：15-25,11.DOI:10.16389/j.cnki.cn42-1737/n.2026.04.002
基于改进注意力机制的粗梗水蕨图像分割方法
Image Segmentation Method for Ceratopteris thalictroides Based on an Improved Attention Mechanism
摘要
Abstract
Ceratopteris thalictroides(C.thalictroides),a representative wetland ecological indicator species in the Hanjiang River Basin,plays an important role in ecosystem functioning and conservation.However,its leaf structure is complex and its posture varies significantly,which poses considerable challenges for image segmentation.To address these issues,this study proposes an improved UNet3+segmentation framework incorporating an enhanced attention mechanism.On the basis of the original spatial and channel self-attention(SCSA)module,an asymmetric convolution structure is introduced to enhance the model's ability to capture the main plant structure and edge details.This modified attention module is embedded into the downsampling stages of UNet3+to strengthen the feature representation of key regions during encoding.A dedicated dataset of C.thalictroides images captured under multiple viewpoints and environmental conditions was constructed,and the model's performance was evaluated using Dice,IoU,F1score,and mIoU.Experimental results show that the proposed SCSAA-UNet3+model achieves superior segmentation performance compared with conventional methods,demonstrating its effectiveness in the image segmentation task of C.thalictroides.关键词
粗梗水蕨/特色生物/图像分割/SCSAA-UNet3+/空间协同注意力/非对称卷积Key words
Ceratopteris thalictroides/characteristic biological resources/image segmentation/SCSAA-UNet3+/spatial collaborative attention/asymmetric convolution分类
农业科技引用本文复制引用
胡慧莉,叶曦,董元火,曾长立..基于改进注意力机制的粗梗水蕨图像分割方法[J].江汉大学学报（自然科学版）,2026,54(4):15-25,11.基金项目
湖北省自然科学基金项目(2023AFB462) （2023AFB462）
江汉大学研究生科研创新基金项目(KYCXJJ202434) （KYCXJJ202434）
