In recent decades, the advancement of deep learning algorithms and their effectiveness in Saliency detection has garnered significant attention in research. Among these methods, U Network ( U-Net ) is widely used in computer vision and image processing. However, most previous deep learning-based Saliency detection methods have focused on the accuracy of salient regions, often overlooking the quality of boundaries, especially fine boundaries. To address this gap, we developed a method to detect boundaries effectively. This method comprises two modules: prediction and residual refinement, based on U-Net structure. The refinement module improves the mask predicted by the prediction module. Additionally, to boost the refinement of the Saliency map, a channel attention module is integrated. This module has a significant impact on our proposed method. The channel attention module is implemented in the refinement module, aiding our network in obtaining a more accurate estimation by focusing on the crucial and informative regions of the image. To evaluate the developed method, five well-known Saliency detection datasets are employed. The proposed method consistently outperforms the baseline method across all five datasets, demonstrating improved performance.