The detection and recognition of small targets are always difficult for researchers in the field of target detection, resulting in the feature extracted from the model not having good expression ability, so the detection result of small targets is poor. This paper presents a modified algorithm based on feature pyramid network(FPN). Specifically, the parallel branch is devised on the original basis to fuse the feature information of two different up-sampling methods to enhance the representation ability of small objects. Meanwhile, a multiple threshold detector named Cascade R-CNN is added to prompt the localization ability of small objects. Experiments are conducted on UAV aerial image datasets. The experimental results reveal that the average precision of the proposed algorithm under MS COCO dataset increases by 9.7 percentage compared to that of the initial FPN algorithm; hence, the proposed algorithm has a good detection performance.