改进的YOLOv3算法对伪装目标检测  

Camouflage Target Detection Based on an Improved YOLOv3 Algorithm

作　　者：吴涛[1] 王伦文 朱敬成 WU Tao;WANG Lun-wen;ZHU Jing-cheng(College of Electronic Countermeasure,National University of Defense Technology,Hefei 230037,China)

机构地区：[1]国防科技大学电子对抗学院,合肥230037

出　　处：《火力与指挥控制》2022年第2期114-120,126,共8页Fire Control & Command Control

摘　　要：针对现有算法对伪装目标检测效率较低的问题,提出了一种基于改进YOLOv3网络的伪装目标检测算法。该算法在现有数据集下,对算法的先验框进行重聚类;依据Darknet53多次利用残差块的特点,将残差网络的级联方式由单级跳连改为多级跳连,改善误差在网络中的回传效果;在网络中添加了注意力模块,增加有用特征的检测权重。实验结果表明,与原始算法相比,改进后的算法模型具有较低的损失值且平均精度均值提高了约4.35%。Aiming at the low efficiency of existing algorithms for camouflage target detection,a camouflage target detection algorithm based on the improved YOLOv3 network is proposed.Firstly,the algorithm re-clusters the priori boxes of the algorithm under the existing datasets;secondly,according to Darknet53 the characteristics of the residual block are utilized many times,the cascading mode of the residual network is changed from Single-stage jump to multi-stage jump connection to improve the return effect of errors in the network;finally,an attention module is added to the network to increase the detection weight of useful features.The experimental results show that compared with the original algorithm,the improved algorithm model has a lower loss value and the mean average precision is increased by about 4.35%.

关 键 词：伪装目标 目标检测 注意力机制 残差网络 

分 类 号：TN957.52[电子电信—信号与信息处理]