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。