Improving Deep Learning-based Saliency Detection Using Channel Attention Module

Abstract

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 develop a method to detect boundaries effectively. This method comprises two modules: prediction and residual refinement, based on the 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.