# Weak Edge Target Segmentation Network Based on Dual Attention Mechanism
###### Abstract
This not only highlights the universality of the U-Net structure but also provides a reliable theoretical foundation and practical support for improving and applying it to segmentation tasks involving objects with weak boundaries, such as glass. However, directly applying existing CNN models to the segmentation of medical consumables made of materials such as glass and plastic still faces challenges. These materials possess unique optical properties, such as transparency and reflectivity, resulting in less pronounced edge information in consumable images, which poses difficulties for feature extraction modules. Moreover, the significant variations in the shape and size of consumables lack consistent structured representations, placing higher demands on the generalization capability of segmentation networks. [PERSON] et al. [19] proposed a Progressive Glass Segmentation Network (PGSNet) to address the challenges in glass segmentation. This method employs a discriminative enhancement module to bridge the characteristic gap between features at different levels, improving the discriminative power of feature representations. Additionally, it utilizes a focus and exploration fusion module to deeply mine useful information during the fusion process by highlighting commonalities and exploring differences, achieving glass segmentation from coarse to fine. [PERSON] et al. [20] introduced a novel bidirectional cross-modal fusion framework for Glass-Like Object (GLO) segmentation. This framework incorporates a Feature Exchange Module (FEM) and a Shifted-Window Cross-Attention Feature Fusion Module (SW-CAFM) in each Transformer block stage. The FEM uses coordinate and spatial attention mechanisms to filter noise and recalibrate features from two modalities. The SW-CAFM fuses RGB and depth features through cross-attention and employs shifted-window self-attention operations to reduce computational complexity. This method achieved promising results on multiple glass and mirror benchmark datasets. [PERSON] et al. [21] proposed a new glass detection method specifically for single RGB images. It extracts backbone features through self-attention methods and then uses a VIT-based deep semantic segmentation architecture called MFT to associate multi-level receptive field features and retain feature information captured at each layer,effectively improving glass region detection capabilities. [PERSON] et al. [22] presented a hybrid learning architecture called YOLOS (You Only Look Once Station Scene), which integrates a novel squeeze-and-excitation (SE) attention module into the detection branch. This module adaptively learns the weights of feature channels, enabling the network to focus more on critical deep features of objects. Despite significant progress in glass and similar object detection, challenges remain in detection accuracy and network generalization capabilities. This research aims to address the limitations of existing methods by proposing innovative solutions to enhance the performance and efficiency of weak edge object segmentation networks. Specifically, this paper focuses on optimizing detection algorithm accuracy while enhancing model adaptability across different scenarios, contributing to technological advancements in this field. Addressing the issues of low accuracy and blurred boundaries encountered by existing deep learning methods in segmenting weak edge targets like glass and plastic poses two major challenges in the image processing process. The first challenge revolves around enhancing the network's feature extraction capabilities to capture the crucial features of the targets, while the second challenge involves maximizing the suppression of redundancy and noise propagation while ensuring feature richness. To tackle these challenges, most improvement efforts focus on strengthening the network's feature representation and information transmission abilities. However, blindly deepening the network structure may lead to the loss of detailed information, which is essential for accurate segmentation.