The target detection system carried on UAV platform often faces many small target detection tasks in practical application.In order to overcome the problems of low detection rate and poor detection accuracy, an improved small target detection algorithm based on YOLOv3 is proposed.Firstly, the K-means clustering algorithm is used to conduct clustering analysis on the remote sensing small target data set from high-altitude perspective, and the number of anchor boxes and corresponding parameters are reset.Then, in the part of feature extraction network, the number of residual blocks after five times of down sampling is reconfigured, and an output scale is introduced in the shallower network, whose feature information is spliced with that in the previous output scale, so as to retain more small target information and edge information.Through the test and analysis of the test data set, the mAP of the improved detection algorithm reaches 92.21%, which is 5.84% higher than that of the original YOLOv3.It effectively solves the problem of YOLOv3 that miss detection is likely to occur on some small targets.