A lightweight target detection network for application to unmanned aerial vehicle （UAV） platforms was proposed for solving the problems of large image-scale variation， small target size， and limited embedded computing resources on UAVs in UAV-side target detection. The network used YOLOv5 as the benchmark model. First， detection branches were used to solve the problem of scale variation. Then， a small-target detection metric based on a mixture of normalized Wasserstein distance and traditional IOU was used for solving the problem of inaccurate small-target detection. In addition， a C3_FN lightweight network structure combining FasterNet and C3 was employed to reduce the computational burden of the network and make it more suitable for UAV platforms. The performance of the algorithms was tested on a simulation platform and an embedded platform using the UAV target detection dataset VisDrone. The simulation platform test results indicate that the proposed network achieves improvements of 6.6% and 4.8% in the mAP_0.5 and mAP_0.5-0.95 metrics， respectively， compared with a benchmark network， and the inference time is only 45.9 ms. The detection results are superior to those of mainstream UAV target detection networks. The test results for the embedded device （NVIDIA Jetson Nano） indicate that the proposed algorithm can achieve high accuracy and near real-time detection performance with limited hardware resources.