摘要
针对低慢小无人机探测任务中精度不高、在嵌入式平台上部署实时性能差的问题,提出了一种基于改进YOLOv4的小型无人机目标检测算法。通过增加浅层特征图、改进锚框、增强小目标,提高网络对小目标的检测性能,通过稀疏训练和模型修剪,大大缩短了模型运行时间。在1080Ti上平均精度(mAP)达到85.8%,帧率(FPS)达75 frame/s,实现了网络轻量化。该模型部署在Xavier边缘计算平台上,可实现60 frame/s的无人机目标检测速度。实验结果表明:与YOLOv4和YOLOv4-tiny相比,该算法实现了运行速度和检测精度的平衡,能够有效解决嵌入式平台上的无人机目标检测问题。
In order to solve the low accuracy in low-slow-small unmanned aerial vehicles(UAVs)mission on embedded platform and deployment problem of poor real-time performance,a small UAV target detection algorithm based on improved YOLOv4 was proposed.By increasing the shallow characteristic figure,improving the anchor,enhancing the small target,and the detection performance of network for small target was improved,through sparse training and model pruning,the model running time was greatly reduced.The average accuracy(mAP)reaches 85.8%on the 1080Ti,and the frame rate(FPS)reaches 75 frame/s,which achieving network lightweight.This lightweight model was deployed on the Xavier edge computing platform,which could achieve the UAV target detection speed of 60 frame/s.Experimental results show that,in compared with YOLOv4 and YOLOv4-TINY,this algorithm achieves the balance of running speed and detection accuracy,and can effectively solve the problem of UAV target detection on embedded platform.
作者
吴璇
张海洋
赵长明
李志朋
王元泽
WU Xuan;ZHANG Haiyang;ZHAO Changming;LI Zhipeng;WANG Yuanze(School of Optics and Photonics,Beijing Institute of Technology,Beijing 100081,China)
出处
《应用光学》
CAS
北大核心
2024年第1期79-88,共10页
Journal of Applied Optics
基金
冬季项目场景三维感知及重建技术(2018YFF0300802)。
关键词
低慢小无人机
目标检测
YOLOv4
剪枝
嵌入式
low-slow-small unmanned aerial vehicles
target detection
YOLOv4
pruning
embedded
