Recently, salient object detection (SOD) methods based on deep learning have achieved significant progress. The existing models often require a considerable amount of memory and time to make precise inferences, SOD is an image preprocessing task, it is obviously unreasonable to consume a large amount of cost on SOD, which makes previous methods difficult to apply in practical scenarios. In this paper, we aim to find a balance between performance and efficiency by designing a lightweight network based on feature pyramid network (FPN), namely MII-FPN, to alleviate this problem. MII-FPN mainly contains a simple multi-scale convolution module and an efficient feature fusion module, which are used to extract and integrate the context information, respectively. Testing on a commonly used six datasets shows that MII-FPN achieves highly competitive accuracy compared with state-of-the-art SOD methods. Besides, the proposed MII-FPN is fairly efficient, which has a frame rate of 299.3FPS on NVIDIA RTX3080 GPU for $336\times 336$ input with only 1.12M parameters. The code is available at https://github.com/XUPTfangjie/MIIFPN