Ship detection is a crucial but challenging task in optical remote sensing images. Recently, thanks to the emergence of deep neural networks, significant progress has been made in ship detection. However, there are still two significant issues that must be addressed: 1) The high-resolution optical images may confuse the background with the ship, leading to more false alarms during detection; 2) The detector receives fewer positive samples due to the sparse and uneven distribution of ships in the optical remote sensing images. In this paper, we innovatively propose employing the saliency information to aid the ship detection task to tackle these two issues. To achieve this goal, we devise two novel modules, Feature-Enhanced Structure (FES) and Saliency Prediction Branch (SPB), to boost the capacity of ship detection in complex environments, and propose a new sampling strategy named Salient Screening Mechanism (SSM) to increase the number of positive samples. More specifically, SSM is adopted during the training phase to mine more positive samples from the ignored set. Then, in an end-to-end learning fashion, a neural network that incorporates our carefully designed FES and SPB is trained to gain more discriminative information for distinguishing the foreground and the background. To evaluate the effectiveness of our proposal, {two new datasets  HRSC-SO and DOTA-isaid-ship} are constructed, which possesses the annotation information for both object detection and saliency detection.  We conduct extensive experiments on the constructed dataset, and the results demonstrate that our method outperforms the previous state-of-the-art approaches.