Salient object detection has attracted considerable attention in the field of the machine vision, with a wide range of applications. This study proposes a salient object detection algorithm based on the dual-attention recurrent convolution to overcome the limitations associated with the existing algorithms, i.e., uneven salient region detection and fuzzy edge representations. A dual-attention module consisting of pixel- and channel-wise attentions is added to a backbone U-Net fully convolutional network to preprocess the shallow convolutional features before skip-layer connection, and reduce noise and clutter interference. This improves its salient region detection performance. Then, following the backbone network, a recurrent convolutional module enhances the edge representation of the prediction region by combining the final prediction map with the shallow convolutional features. The results of experiments on three open datasets show that the proposed algorithm is better able to highlight salient regions and refine their edges than other correlation algorithms.