面向多无人机协同的多模态目标检测方法  

Multimodal target detection method for multi-UAV coordination

作　　者：孙晓永 孙备 郭润泽 党昭洋 周沛达 Sun Xiaoyong;Sun Bei;Guo Runze;Dang Zhaoyang;Zhou Peida(College of Intelligence Science and Technology,National University of Defense Technology,Changsha 410072,China)

机构地区：[1]国防科技大学智能科学学院,长沙410072

出　　处：《仪器仪表学报》2025年第2期209-220,共12页Chinese Journal of Scientific Instrument

摘　　要：为解决现阶段单无人机目标检测过程中存在的探测视场有限、目标易被遮挡、单一光源图像信息薄弱等问题,提升无人机可靠、高效的感知计算能力,提出一种面向多无人机协同的多模态目标检测方法。首先,研究可见光和红外融合的多模态目标检测算法,提出了由视觉任务驱动的基于卷积融合网络的双光融合模型,将融合图像经过语义分割网络的结果反馈给融合网络,通过对融合网络参数的迭代训练出损失较小的双光图像融合模型。然后对融合后的图像输入视觉感知增强模块进行图像增强,消除了不良光照条件对图像质量的影响,提升了目标细节特征保持性,并在MSRS数据集验证了算法的有效性。此外,面向多无人机协同检测提出了基于分布式生物感知处理的主动感知流程,通过无人机在被感测目标位置计算检测置信度并通过释放信息素来分配主机和从机的检测优先级,完成多无人机协同检测任务的引导策略,实现不同光照条件下非结构化地面场景的目标检测。实验结果表明,该算法在无人机载智能边缘计算平台RK3588上具有56.55 ms延迟和45.84 fps的推理速度,能准确检测地面场景布设的典型军事目标,平均检测精度达到78.5%。To solve problems such as limited detection field of view,easy occlusion of target and weak image information of single light source in the current process of single unmanned aerial vehicle(UAV)target detection,and improve the reliable and efficient perception and computing capability of UAV,a multi-modal target detection method for multi-UAV cooperation is proposed in this article.Firstly,a multimodal object detection algorithm based on visible light and infrared fusion is studied,and a dual light fusion model based on a convolutional fusion network driven by visual tasks is proposed.The fused image is fed back to the fusion network through the semantic segmentation network,and a dual light image fusion model with a smaller loss is iteratively trained on the fusion network parameters.Then,the fused image is input into the visual perception enhancement module for image enhancement,eliminating the impact of poor lighting conditions on image quality and improving the preservation of target detail features.The effectiveness of the algorithm is verified on the MSRS dataset.In addition,an active perception process based on distributed biosensing processing is proposed for multi-drone collaborative detection.By calculating the detection confidence of the drone at the location of the sensed target and allocating the detection priority of the host and slave through the release of pheromones,a guidance strategy for multi-drone collaborative detection tasks is completed,achieving target detection in unstructured ground scenes under different lighting conditions.Experimental results show that the algorithm has a 56.55 ms delay and 45.84 fps reasoning speed on the unmanned aerial intelligent edge computing platform RK3588,and can accurately detect typical military targets deployed in ground scenes,with an average detection accuracy of 78.5%.

关 键 词：多模态数据 多无人机协同 主动感知 目标检测 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TH701[自动化与计算机技术—计算机科学与技术]