For the detection of small remote sensing targets with complex backgrounds， an improved multi-scale feature fusion-based single shot multi-box detector （SSD） method was proposed. First， a feature map fusion mechanism was designed to fuse the shallow high-resolution feature maps and deep feature maps with rich semantic information， after which feature pyramids were built between the feature maps to enhance small target features. Subsequently， the channel attention module was introduced to overcome the background interference by constructing a weight parameter space to provide more attention to the channels that focus on the target region. Finally， the scale between the priori box and the original map was adjusted to better fit the small remote sensing target scale. Qualitative and quantitative tests based on image datasets from a remote sensing aircraft were then performed， with the results showing that the proposed method improves the detection accuracy by 4.3% when compared with the SSD method and can adapt to complex multi-scale remote sensing target detection tasks without reducing the detection rate for small targets.