士兵目标的少样本深度学习检测方法

A deep learning method detection for target with few samples based on YOLOv3 was proposed to solve the problem of small enemy soldiers' datasets. Data augmentation was used to improve the robustness of the small-sample target detection model, and improve the network structure by connecting the shallow network feature map to the deep network across layers. k-means clustering was used to obtain anchor boxes suitable for soldier target characteristics, and pre-training was used to improve the convergence speed of model training. The results show that the method in this paper has a success rate (mAP) of 85.6% for target detection of enemy soldiers with small enemy soldiers' datasets, a detection accuracy (IOU) of 82.18%, and a good detection effect for small and occluded targets. The detection speed deployed on NVIDIA TITAN V GPU computer and NVIDIA Xavier reaches 54.6 and 26.8 fps, which means a good real-time performance.