# Weak Edge Target Segmentation Network Based on Dual Attention Mechanism
###### Abstract  
Segmentation of weak edge targets such as glass and plastic poses a challenge in the field of target segmentation. The detection process is susceptible to background interference and various external factors due to the transparent nature of these materials. To address this issue, this paper introduces a segmentation network for weak edge target objects (WETS-Net). To effectively extract edge information of such objects and eliminate redundant information during feature extraction, a dual-attention mechanism is employed, including the Edge Attention Extraction Module (EAEM) and the Multi-Scale Information Fusion Module (MIFM). Specifically, the EAEM combines improved edge feature extraction kernels to selectively enhance the importance of edge features, aiding in more precise target region extraction. The MIFM utilizes spatial attention mechanisms to fuse multi-scale features, reducing background and external interference. These innovations enhance the performance of WETS-Net, offering a new direction for weak edge target segmentation research. Finally, through ablation experiments, the effectiveness of each module is effectively validated. Moreover, the proposed algorithm achieves an average detection accuracy of 95.83% and 96.13% on the dataset and a self-made dataset, respectively, outperforming similar U-Net-improved networks. target segmentation; attention mechanism; edge features; multi-scale feature fusion +
Footnote †: journal: Applied  
0000-0001-9993-389  
(FCNs) has pioneered end-to-end semantic segmentation [10]. Building upon this foundation, U-Net [11] further introduced the encoder-decoder structure and skip connection mechanism, significantly enhancing the accuracy of image semantic segmentation. In recent years, U-Net has gained widespread adoption in the field of object segmentation due to its exceptional performance, attracting considerable attention from researchers. [PERSON] et al. [12] demonstrated the significant effectiveness of U-Net and its variants in building segmentation. Their study compared various CNN and Transformer models, including U-Net, Residual U-Net, and Attention Residual U-Net. These models achieved excellent segmentation performance across different datasets, highlighting the adaptability and potential of the U-Net architecture in handling complex scene segmentation tasks. [PERSON] et al. [13] showcased U-Net's powerful real-time multi-object segmentation capabilities in remote sensing and surveillance applications, particularly excelling in processing drone aerial imagery. [PERSON] et al. [14] proposed an improved U-Net model that integrated the advantages of DenseNet, dilated convolutions, and DeconvNet for remote sensing image semantic segmentation, exhibiting outstanding performance on the Potsdam orthophoto dataset. [PERSON] et al. [15] applied U-Net to brain tumor detection and segmentation, demonstrating superior performance through joint work with YOLOv5. [PERSON] et al. [16] introduced a dual U-Net framework, innovatively combining object region and boundary information for segmentation, showing advantages in tasks such as lung, heart, and clavicle segmentation in chest X-rays, as well as segmentation of subtle edges like pelvic structures. [PERSON] et al. [17] incorporated attention mechanisms into U-Net for deforestation detection in the Amazon rainforest and Atlantic forest of South America. The Attention U-Net outperformed traditional U-Net and other baseline models on Sentinel-2 satellite imagery. [PERSON] et al. [18] enhanced the U-Net model by introducing attention mechanisms and residual modules, making it more suitable for the semantic segmentation of moving objects. U-Net and its derivative networks have been widely applied in diverse detection scenarios, fully demonstrating the versatility and adaptability of this architecture.