Aiming at the current problem of low performance as well as missed and false detection of small targets in Unmanned Aerial Vehicle （UAV） perspective， an improved BDS-YOLO （BiFPN-Dual-Small target detection-YOLO） model based on YOLOv8 was proposed. Firstly， RepViTBlock （Revisiting mobile CNN from ViT perspective Block） and EMA （Efficient Multi-scale Attention） mechanism were used to construct C2f-RE （C2f-RepViTBlock Efficient multi-scale attention） to improve deep C2f （faster implementation of CSP bottleneck with 2 Convolutions） module in the backbone network， thereby enhancing the model’s ability to extract small target features and reducing the number of parameters. Secondly， BiFPN （Bi-directional Feature Pyramid Network） was used to reconstruct the neck network， so that features at different levels were able to be fused with each other. Thirdly， a dual small target detection layer was constructed on the basis of the improved neck network， and the layer was combined with shallow and shallowest features to improve detection ability of the model for small targets. Finally， the improved loss function Inner-EIoU （Inner-Efficient-Intersection over Union） was introduced. In this function， a more reasonable aspect ratio measure method was used and the limitations of IoU （Intersection over Union） itself were addressed. Experimental results show that compared to the original model on VisDrone2019 dataset， the improved model improves the precision， recall， mAP@50， and mAP@50：95 by 8.5， 7.7， 9.2， and 6.3 percentage points， respectively， with parameters of only 2.23×106， which means a reduction in model size of 19.1%. It can be seen that the proposed model improves performance significantly while achieving certain lightweight.
