显著性目标检测是计算机视觉领域热门研究之一，而显著性目标检测在处理背景复杂的图像时存在检测目标边缘模糊的问题。提出了一种多尺度特征信息融合的网络结构U²Net_MFF（U²Net_multi-scale feature fusion）。该网络通过对每层解码器增加跳跃连接，使每层解码器不仅能够获得上一级解码器的特征还能获得本层以上所有更浅层次的特征信息，以此来获取目标区域更多的细节特征，提高网络的检测精度；将动态上采样DySample融入到残差U型块中来提高上采样后特征图的质量，并结合多维度协作注意力机制抑制图像复杂背景干扰的同时提升对显著性目标区域的敏感程度。实验表明，改进后的算法在准确率、精确率、召回率、均交并比和F₁分数上分别提升了1%、0.3%、0.4%、0.7%和0.8%，证明该算法具有更强的抗干扰能力和更清晰的目标边界检测效果。

Salient object detection is one of the hot research topics in the field of computer vision. However, when handling images with complex backgrounds, salient object detection often faces the issue of blurred object boundaries. A multi-scale feature fusion network structure, named U²Net_MFF, was proposed. The network introduced skip connections at each decoder layer, allowing each decoder to not only obtain the features from the previous decoder but also acquire feature information from all shallower layers above. This approach enabled the network to capture more detailed features of the target region, thereby improving detection accuracy. Additionally, dynamic upsampling (DySample) was incorporated into the residual U-shaped blocks to enhance the quality of the upsampled feature maps. A multi-dimensional collaborative attention mechanism was also employed to suppress interference from complex backgrounds, while increasing sensitivity to the salient object regions. Experimental results show that the improved algorithm achieves increases of 1%, 0.3%, 0.4%, 0.7%, and 0.8% in accuracy, precision, recall, mean intersection-over-union (MIoU), and F₁ score, respectively. These results demonstrate that the algorithm has stronger anti-interference ability and produces clearer object boundary detection.