士兵和装甲车目标多尺度检测方法

针对士兵和装甲车目标的尺度差异大以及目标距离远近造成的目标多尺度问题，以YOLOv4深度学习算法为基础，提出了一种多尺度目标检测方法. 通过针对性的数据增强方法丰富小目标样本的多样性，对输入图像进行分割预处理以提高网络输入小目标的分辨率，并基于特征金字塔网络实现大、中、小目标的分离检测，最后匹配检测结果并进行NMS处理去除冗余检测框，从而实现多尺度目标检测. 实验结果表明，本文方法在保持大目标检测效果的情况下，中、小目标的平均检测精度分别提升了1.20%和5.54%，有效提高了中、小目标的检测效果. 

Multi-Scale Detection Method for Soldier and Armored Vehicle Objects

A multi-scale object detection method was proposed based on YOLOv4 deep learning algorithm to solve the multi-scale problem caused by the huge-scale difference between soldiers and armored vehicles, as well as object distance. The diversity of small object samples was enriched through targeted data augmentation methods input images were segmented to improve the resolution of input small objects of network, the detection results of large, medium and small objects were separated based on the feature pyramid network, and finally the detection results were matched and NMS processing was carried out to remove the redundant detection boxes, so as to achieve multi-scale object detection. The experimental results show that the average mean precision of small and medium objects is improved by 1.20% and 5.54% respectively, while the detection effect of large objects is maintained, which effectively improves the detection effect of small and medium objects.