Although methods based on the fully convolutional neural networks (FCNs) have shown strong advantages in the field of salient object detection, the existing methods still have two challenging issues: insufficient multi-level feature fusion ability and boundary blur. To overcome these issues, we propose a novel salient object detection method based on a multi-feature fusion cross network (denoted MFC-Net). Firstly, to overcome the issue of insufficient multi-level feature fusion ability, inspired by the connection mode of human brain neurons, we propose a novel cross network framework, combined with contextual feature transfer modules (CFTMs) to integrate, enhance and transmit multi-level feature information in an iterative manner. Secondly, to address the issue of blurred boundaries, we effectively enhance the edge features of saliency map by a simple edge enhancement strategy. Thirdly, to reduce the loss of information caused by the saliency map generated by multi-level feature fusion, we use feature fusion modules (FFMs) to learn contextual feature information from multiple angles and then output the resulting saliency map. Finally, a hybrid loss function fully supervises the network at the pixel and object level, optimizing the network performance. The proposed MFC-Net has been evaluated using five benchmark datasets. The performance evaluation demonstrates that the proposed method outperforms other state-of-the-art methods, which proves the superiority of MFC-Net approach.

尽管基于全卷积神经网络（FCNs）的方法在显着目标检测领域表现出很强的优势，但现有方法仍然存在两个具有挑战性的问题：多级特征融合能力不足和边界模糊。为了克服这些问题，我们提出了一种基于多特征融合交叉网络（表示为 MFC-Net）的新型显着目标检测方法。首先，为了克服多层次特征融合能力不足的问题，受人脑神经元连接模式的启发，我们提出了一种新的跨网络框架，结合上下文特征转移模块（CFTM）来整合、增强和传输多层次特征。以迭代的方式对特征信息进行分级。其次，为了解决边界模糊的问题，我们通过简单的边缘增强策略有效地增强了显着图的边缘特征。第三，为了减少多级特征融合生成的显着图造成的信息丢失，我们使用特征融合模块（FFM）从多个角度学习上下文特征信息，然后输出所得的显着图。最后，混合损失函数在像素和对象级别全面监督网络，优化网络性能。已使用五个基准数据集对提议的 MFC-Net 进行了评估。性能评估表明，所提出的方法优于其他最先进的方法，这证明了 MFC-Net 方法的优越性。我们使用特征融合模块（FFM）从多个角度学习上下文特征信息，然后输出结果显着图。最后，混合损失函数在像素和对象级别全面监督网络，优化网络性能。已使用五个基准数据集对提议的 MFC-Net 进行了评估。性能评估表明，所提出的方法优于其他最先进的方法，这证明了 MFC-Net 方法的优越性。我们使用特征融合模块（FFM）从多个角度学习上下文特征信息，然后输出结果显着图。最后，混合损失函数在像素和对象级别全面监督网络，优化网络性能。已使用五个基准数据集对提议的 MFC-Net 进行了评估。性能评估表明，所提出的方法优于其他最先进的方法，这证明了 MFC-Net 方法的优越性。