<p>Salient object detection (SOD) aims to imitate the human visual system (HVS) to accurately locate and segment salient objects in the scenes. In particular, it has been very successful detection results in fully supervised salient object detection (FSOD) methods, but they rely on great labor costs to provide pixel-by-pixel labels. The unsupervised salient object detection (USOD) methods do not utilize artificial labels to detect the salient objects in the scenes to avoid the drawback of FSOD. The initial pseudo-labels required by current deep learning-based USOD inevitably introduce some non-salient noise and ignore some fine-grained information of salient objects. In this paper, we propose EFNet, a novel two-stage USOD method. Its core lies in an innovative pseudo-label initialization stage. This stage employs an Enhanced Activation Strategy (EAS) based on a hybrid attention mechanism to increase the weight of salient regions, reduce the weight of non-salient regions, and guide the model to efficiently extract salient knowledge from shallow to deep layers. Meanwhile, a Fine-grained Information Auxiliary Strategy (FIAS) based on upper and lower branches is introduced to capture the boundary and texture information of salient objects, thereby generating cleaner and more accurate pseudo-labels for training in the second stage. Extensive experiments conducted on five benchmark datasets prove that our method achieves state-of-the-art USOD performance.</p>