摘要
为解决小型无人机“黑飞”对公共领域的威胁问题。基于无人机目标多模态图像信息,文中提出一种轻量化多模态自适应融合孪生网络(Multimodal adaptive fusion Siamese network,MAFS)。设计一种全新的自适应融合策略,该模块通过定义两个模型训练参数赋予不同模态权重以实现自适应融合;本文在Ghost PAN基础上进行结构重建,构建一种更适合无人机目标检测的金字塔融合结构。消融实验结果表明本文算法各个模块对无人机目标检测精度均有提升,多算法对比实验结果表明本文算法鲁棒性更强,与Nanodet Plus-m相比检测时间基本不变的情况下m AP提升9%。
To address the threat of small drones"black flying"to the public domain.Based on the multimodal image information of an unmanned aerial vehicle(UAV)target,a lightweight multimodal adaptive fusion Siamese network is proposed in this paper.To design a new adaptive fusion strategy,this module assigns different modal weights by defining two model training parameters to achieve adaptive fusion.The structure is reconstructed on the basis of a Ghost PAN,and a pyramid fusion structure more suitable for UAV target detection is constructed.The results of ablation experiments show that each module of the algorithm in this study can improve the detection accuracy of the UAV targets.Multi-algorithm comparison experiments demonstrated the robustness of the algorithm.The mAP increased by 9%when the detection time was basically unchanged.
作者
韩自强
岳明凯
张骢
高棋
HAN Ziqiang;YUE Mingkai;ZHANG Cong;GAO Qi(Shenyang Ligong University,College of equipment Engineering,Shenyang 110159,China)
出处
《红外技术》
CSCD
北大核心
2023年第7期739-745,共7页
Infrared Technology
基金
辽宁省教育厅基本科研面上项目(LJKMZ20220605)。
