In order to solve the problem of low detection accuracy caused by small size and dense distribution of targets, complex detection scene and target occlusion from the viewpoint of UAV aerial photography, an SSD-based small target detection algorithm is proposed. Firstly, the SE-ResNet50 network is used to replace VGG16 as the backbone network, and the model pays more attention to useful channel information by learning the adaptive channel weights. Then, the anchor frame parameters of the shallow-layer network are modified to improve the performance of small target detection. In the shallow-layer network, two combinations of SE+SAM and CBAM are adopted to pay attention to the images from the two dimensions of channel and space. Finally, EIoU is used instead of traditional IoU, to calculate the intersection over union ratio, and the FocalL1 loss function is adopted. Then, the EIoU and FocalL1 loss functions are integrated to obtain final Focal-EIoU. VisDrone2019 dataset is used for verification. In comparison with that of traditional SSD algorithm, the precision, recall and mAP is increased by 11. 7, 11. 2 and 9. 8 percentage points respectively, and FPS is increased by 5 frames per second, which verifies the effectiveness of the algorithm in small target detection.