摘要
红外无人机目标检测在军民领域的应用前景广阔。由于无人机目标尺度较小,空中环境复杂多变,目前普遍存在检测率低和误报率高的现象。针对复杂场景下红外无人机目标检测不良等问题,本文提出ST-YOLOA目标检测模型。首先,使用Swin Transformer网络架构和协调注意力(CA)机制搭建STCNet骨干特征提取网络;其次,特征融合部分采用带残差结构的PANet路径聚合网络构建特征金字塔提升整体特征提取能力,同时改进了上下采样方式以增强检测能力;最后,使用解耦检测头预测无人机目标的位置。试验结果表明,本文提出的模型检测精度为92.8%,检测速度达到了22帧/s,这表明该模型与其他模型相比具有较好的检测效果,且基本满足实时性检测要求,对于多无人机目标场景下的检测具有现实意义。
Infrared drone target detection has broad application prospects in both military and civilian fields,and it is a hot research topic in the field of computer vision.Due to the small scale of drone targets and the complex and ever-changing aerial environment,existing detection algorithms generally have low detection rates and high false alarm rates.Aiming at issues such as poor detection of infrared drone targets in complex scenarios,this article proposes a ST-YOLOA infrared unmanned aerial vehicle target detection model.Firstly,in order to improve model performance and effectively capture global information,an STCNet backbone feature extraction network is constructed using the Swin Transformer network architecture and coordinated attention(CA)mechanism;Secondly,in the feature fusion section,a PANet path aggregation network with residual structure is used to construct a feature pyramid to enhance the overall feature extraction ability,while improving the up and down sampling method to enhance detection ability;Finally,the decoupled detection head is used to predict the position of the drone target.The proposed model has a detection accuracy of 92.8%and a detection speed of 22frames/s,which is verified by experiments on an infrared drone dataset.This indicates that the model has better detection performance compared to other models,especially in complex environments,and basically meets the real-time detection requirements.It has practical significance for detection in multi drone target scenarios.
作者
王思宇
卢瑞涛
黄攀
杨小冈
夏文新
李清格
张震宇
Wang Siyu;Lu Ruitao;Huang Pan;Yang Xiaogang;Xia Wenxin;Li Qingge;Zhang Zhenyu(Rocket Force University of Engineering,Xi’an 710025,China;Science and Technology on Electro-optic Control Labotatory,Luoyang 471000,China;AVIC Xi’an Aeronautics Computing Technique Research Institute,Xi’an 710068,China)
出处
《航空科学技术》
2024年第2期39-46,共8页
Aeronautical Science & Technology
基金
国家自然科学基金(62276274)
航空科学基金(201851U8012)
陕西省自然科学基金(2023-JC-YB-528)。
