# Weak Edge Target Segmentation Network Based on Dual Attention Mechanism
## 2 Theoretical Basis  
### U-Net Semantic Segmentation Network  
U-Net is a convolutional neural network architecture proposed by [PERSON] et al. in 2015 for medical image segmentation [23]. This network, based on an encoder-decoder structure, introduces skip connections to facilitate multi-scale feature fusion and effective spatial information transmission. With its outstanding segmentation performance and end-to-end training approach, U-Net has garnered widespread success in the field of medical image segmentation and is considered one of the milestone works in this domain. The core structure of U-Net embodies a symmetrical encoder-decoder architecture [24], showcased in Figure 1. The encoder segment comprises a sequence of convolution and downsampling operations, aimed at deriving multi-scale feature representations from the input image. Each downsampling step reduces the resolution of the feature map by half while doubling the channel count, enabling the network to progressively capture increasingly abstract and high-level semantic information. Conversely, the decoder segment employs a series of upsampling and convolution operations to systematically restore the spatial resolution of the feature map until it aligns with the dimensions of the input image. This symmetrical design empowers U-Net to encode high-level semantic features while preserving ample spatial detail, laying a solid groundwork for precise image segmentation. Delving deeper into the structure, the encoder undertakes the task of feature extraction from the input image through convolutional layers, followed by downsampling techniques like max pooling or strided convolution. This downsampling process diminishes the spatial dimensions of the feature map, enabling the network to progressively concentrate on broader areas of the input image and facilitating the assimilation of more abstract features and the hierarchical representation of the input data. On the contrary, the decoder is entrusted with the responsibility of upsampling the feature map to the original input image's resolution. This restoration of spatial information holds paramount importance in crafting segmentation masks that conform to the input image's scale. The decoder accomplishes this through the utilization of transposed convolutions or upsampling layers, aiding in the recovery of details lost during the downsampling phase. The hallmark of the U-Net architecture lies in its symmetrical framework, which adeptly integrates high-level semantic insights from the encoder with spatial intricacies reconstructed by the decoder. This design enables U-Net to effectively capture both global context and local nuances, rendering it exceptionally well-suited for tasks necessitating precise segmentation, such as medical image analysis and semantic segmentation of weak edge objects. Figure 1: Encoder–decoder architecture. Another design of U-Net is the introduction of a jump connection mechanism between the encoder and the decoder [25]. Specifically, each lower sampling layer in the encoder is directly connected to the corresponding upper sampling layer in the decoder, allowing the high-resolution feature map extracted by the encoder to be connected to the feature map generated by the decoder. This design effectively combines local spatial information with global context information and reduces the spatial information loss usually associated with downsampling. In addition, jump joins act like residual learning, helping gradients propagate more smoothly throughout the network, thus speeding up model convergence and optimization. In addition, these jump connections enable the network to learn more robust and complex representations by allowing information to bypass certain layers, preserving finer details, and helping pinpoint features during segmentation. This mechanism enhances the network's ability to capture fine-grained detail and overall context, greatly improving its performance in tasks that require complex image segmentation.