# Weak Edge Target Segmentation Network Based on Dual Attention Mechanism
## 5 Conclusions  
The paper introduces a novel dual-attention mechanism semantic segmentation network called WETS-Net to address the challenges faced in weak edge target image segmentation tasks, such as transparent consumables. Built upon the U-Net architecture, this network incorporates the EAEM and MIFM modules to enhance texture details in shallow features and fuse contextual information across different scales. The EAEM module employs a spatial attention mechanism to adaptively adjust the weights of different regions in shallow feature maps, emphasizing texture details of weak edge targets while  
Figure 9: Comparison of segmentation effect of WETS-Net.  
suppressing background interference. The design of EAEM integrates the EE-Conv convolutional kernel, enabling the network to better capture edge features of targets and improve segmentation accuracy.  
In the decoding phase, WETS-Net enhances the skip connections of U-Net by introducing the MIFM module at each connection point. This allows the network to adaptively fuse high-level semantic features extracted by the encoder with high-resolution spatial details restored by the decoder, reducing the impact of blurry boundaries and background noise on segmentation results. MIFM utilizes a lightweight channel attention mechanism to adjust channel weights in different scale feature maps adaptively, achieving cross-scale feature fusion and optimization. Compared to existing channel attention mechanisms, MIFM significantly reduces parameter and computational overhead while maintaining performance, making it more suitable for real-time segmentation tasks.  
To further improve segmentation performance, a multi-task learning strategy is employed, incorporating a composite loss function consisting of primary and auxiliary losses. The auxiliary loss supervises the learning process of the EAEM and MIFM modules, directing the network's focus towards the feature extraction and fusion performance of these key modules.  
Experimental results on the Trans10K dataset and a custom dataset demonstrate that WETS-Net achieves significant performance improvements in weak edge target image segmentation tasks, surpassing existing semantic segmentation methods. Through ablation studies, the effectiveness of the proposed modules is further validated. However, the paper acknowledges the limitations of CNN-based methods in global semantic understanding despite their strong local feature extraction capabilities and suitability for small-scale datasets. Future research directions could explore enhancing the network's perception and understanding of key target features, such as by introducing Transformer-based global attention mechanisms or designing more efficient cross-scale feature fusion strategies.  
N.W. conceptualization, methodology, software, writing--original draft preparation, manuscript writing, visualization, investigation, writing--reviewing and editing. D.J.: supervision. Z.L.: data curation, investigation. Z.H.: software, validation. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Natural Science Lateral Project of Beijing Jiaotong University (W20L00160).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare that they have no competing interests.