  Salient object detection (SOD) remains an important task in computer vision,
with applications ranging from image segmentation to autonomous driving. Fully
convolutional network (FCN)-based methods have made remarkable progress in
visual saliency detection over the last few decades. However, these methods
have limitations in accurately detecting salient objects, particularly in
challenging scenes with multiple objects, small objects, or objects with low
resolutions. To address this issue, we proposed a Saliency Fusion Attention
U-Net (SalFAU-Net) model, which incorporates a saliency fusion module into each
decoder block of the attention U-net model to generate saliency probability
maps from each decoder block. SalFAU-Net employs an attention mechanism to
selectively focus on the most informative regions of an image and suppress
non-salient regions. We train SalFAU-Net on the DUTS dataset using a binary
cross-entropy loss function. We conducted experiments on six popular SOD
evaluation datasets to evaluate the effectiveness of the proposed method. The
experimental results demonstrate that our method, SalFAU-Net, achieves
competitive performance compared to other methods in terms of mean absolute
error (MAE), F-measure, s-measure, and e-measure.
