摘要
YOLOv3目标检测模型对于巡飞弹作战中的军事集群目标存在可能漏检紧邻目标的问题。改进算法以YOLOv3为基础,对其候选框选择算法采用的非极大值抑制(NMS)引入惩罚函数,实现soft-NMS,从而减少紧邻目标识别边框被误删的概率。同时针对军事目标数据稀缺的情况,对数据的预处理采用k-fold交叉验证策略,抑制过拟合现象,充分训练模型。实验结果表明,改进算法后对集群目标的检测效果要好于原YOLOv3,其准确率提高了3.14%,召回率提高了17.58%,符合巡飞弹作战中对目标检测精度指标的要求。
The YOLOv3 target detection model may miss detecting the adjacent targets for military cluster targets in loitering munitions patrol operations.Based on YOLOv3,a penalty function is introduced into the non-maximum suppression(NMS)of the candidate frame selection algorithm to realice soft-nms,so as to reduce the probability that the adjacent target recognition frame is deleted by mistakes.At the same time,in view of the scarcity of military target data,k-fold cross-validation strategy is adopted for data preprocessing to suppress the over-fitting phenomenon and to fully train the model.Experimental results show that the recognition effect with the improved algorithm is better than that of the original YOLOv3.Its precision and recall are increased by 3.14%and 17.58%respectively,which is in line with the requirements of target detection precision index in loitering munition operations.
作者
张奔
徐锋
李晓婷
赵彦东
ZHANG Ben;XU Feng;LI Xiao-ting;ZHAO Yan-dong(North Automatic Control Technology Institute,Taiyuan 030006,China)
出处
《火力与指挥控制》
CSCD
北大核心
2021年第5期81-85,共5页
Fire Control & Command Control
基金
国防基础科研基金资助项目(JCKY2017208B018)。
