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.

我们提出了一种名为Saliency Fusion Attention U-Net (SalFAU-Net)的模型，它在每个注意力U-net模型的解码器块中合并了一个显著性融合模块，从而可以准确地检测具有多个对象、小物体或低分辨率对象的具有挑战性场景的显著对象。我们通过使用二值交叉熵损失函数在DUTS数据集上训练SalFAU-Net，并在六个流行的SOD评估数据集上进行实验以评估所提方法的有效性。实验结果表明，我们的方法在平均绝对误差（MAE）、F-值、s-值和e-值方面与其他方法相比具有竞争性的性能。

SalFAU-Net：显著性融合关注 U-Net 用于显著目标检测