Abstract: Due to the characteristics of low pixels and little information carried by small ground targets, the target detection algorithm on the conventional data set is directly applied to the detection of small ground targets, which will result in missing and wrong detection. Aimed at this problem, this paper proposed an improved YOLOv3 algorithm for ground small target detection based on deep learning algorithm. In order to select the appropriate number and size of anchor boxes, K-means clustering algorithm was used to analyze the targets in the dataset. By adding a feature map for detecting small targets, the feature fusion target detection layer was established, which improved the YOLOv3 network detection scale. On the detection experiment of remote sensing data set of DOTA, the improved YOLOv3 algorithm and the YOLOv3 algorithm were compared. The results show that the improved algorithm can effectively detect small targets on the ground, the precision is improved by 17.04%, and the recall rate is increased by 6.44%. Comparing with the Faster R-CNN algorithm, the mAP of the improved YOLOv3 algorithm is improved by 12.7%. When the improved YOLOv3 algorithm was trained and dropout optimization mechanism was added, its mAP score on the test set was improved by 3.24%.