无人机作战平台的智能目标识别方法
|
摘要
深入研究了流行的目标识别方法YOLOv3,将Inception模块融入其特征提取网络darknet-53中,从而得到新网络darknet-139。相比YOLOv3特征提取网络,新网络具有更好的特征提取能力。采集并制作算法所需的数据集,分别在YOLOv3和本文算法上进行训练并测试。实验结果表明,相比YOLOv3,本文算法的平均识别率提升了约2%。
The popular target recognition method YOLOv3 is deeply studied,and the Inception module is integrated into the feature extraction network darknet-53 to get a new network darknet-139.Compared with YOLOv3,the new network has better ability in feature extraction.The data set required by the algorithm is collected and made,and were trained and tested on YOLOv3 and the proposed algorithm,respectively.The experimental results show that the average recognition rate of the proposed algorithm is about 2% higher than that of YOLOv3.
The popular target recognition method YOLOv3 is deeply studied,and the Inception module is integrated into the feature extraction network darknet-53 to get a new network darknet-139.Compared with YOLOv3,the new network has better ability in feature extraction.The data set required by the algorithm is collected and made,and were trained and tested on YOLOv3 and the proposed algorithm,respectively.The experimental results show that the average recognition rate of the proposed algorithm is about 2% higher than that of YOLOv3.
引文
[1]Du Y L,Li J Z,Zhang Y,et al.Overview of target recognition algorithms in UAV battlefield reconnaissance[J].Aerodynamic Missile Journal,2016(7):38-41,53.杜玉龙,李建增,张岩,等.无人机战场侦察中目标识别算法研究综述[J].飞航导弹,2016(7):38-41,53.
[2]Ou P,Zhang Z,Lu K,et al.Object detection in remote sensing images based on convolutional neural networks[J].Laser&Optoelectronics Progress,2019,56(5):051002.欧攀,张正,路奎,等.基于卷积神经网络的遥感图像目标检测研究[J].激光与光电子学进展,2019,56(5):051002.
[3]Zhang H,Wang K F,Wang F Y.Advances and perspectives on applications of deep learning in visual object detection[J].Acta Automatica Sinica,2017,43(8):1289-1305.张慧,王坤峰,王飞跃.深度学习在目标视觉检测中的应用进展与展望[J].自动化学报,2017,43(8):1289-1305.
[4]Gu Y,Xu Y.Architecture design of deep convolutional neural network for SAR target recognition[J].Journal of Image and Graphics,2018,23(6):928-936.谷雨,徐英.面向SAR目标识别的深度卷积神经网络结构设计[J].中国图象图形学报,2018,23(6):928-936.
[5]Russakovsky O,Deng J,Su H,et al.ImageNet large scale visual recognition challenge[J].International Journal of Computer Vision,2015,115(3):211-252.
[6]Uijlings J R R,van de Sande K E A,Gevers T,et al.Selective search for object recognition[J].International Journal of Computer Vision,2013,104(2):154-171.
[7]Redmon J,Farhadi A.YOLO9000:better,faster,stronger[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2017:6517-6525.
[8]Sun H Z,Chang T Q,Wang Q D,et al.Image detection method for tank and armored targets based on hierarchical multi-scale convolution feature extraction[J].Acta Armamentarii,2017,38(9):1681-1691.孙皓泽,常天庆,王全东,等.一种基于分层多尺度卷积特征提取的坦克装甲目标图像检测方法[J].兵工学报,2017,38(9):1681-1691.
[9]Lim T R,Guntoro A T.Car recognition using Gabor filter feature extraction[C]∥Asia-Pacific Conference on Circuits and Systems,2002:451-455.
[10]Chen L L,Zhang Z D,Peng L.Real time detection method based on improved single shot multibox detector[J].Laser&Optoelectronics Progress,2019,56(1):011002.陈立里,张正道,彭力.基于改进SSD的实时检测方法[J].激光与光电子学进展,2019,56(1):011002.
[11]Girshick R.Fast R-CNN[C]∥IEEE International Conference on Computer Vision,2015:1440-1448.
[12]Ren S Q,He K M,Girshick R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149.
[13]Redmon J,Divvala S,Girshick R,et al.You only look once:unified,real-time object detection[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2016:779-788.
[14]Redmon J,Farhadi A.Yolov3:an incremental improvement[J].arXiv preprint arXiv:1804.02767,2018.
[15]He K M,Zhang X Y,Ren S Q,et al.Deep residual learning for image recognition[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2016:770-778.
[16]Szegedy C,Ioffe S,Vanhoucke V,et al.Inceptionv4,inception-Resnet and the impact of residual connections on learning[C]∥AAAI Conference on Artificial Intelligence(AAAI-17),2017:4278-4284.
[17]Wei Y M,Quan J C,Hou Y Q Y.Aerial image location of unmanned aerial vehicle based on YOLOv2[J].Laser&Optoelectronics Progress,2017,54(11):111002.魏湧明,全吉成,侯宇青阳.基于YOLO v2的无人机航拍图像定位研究[J].激光与光电子学进展,2017,54(11):111002.
[18]Hinton G,Deng L,Yu D,et al.Deep neural networks for acoustic modeling in speech recognition:the shared views of four research groups[J].IEEESignal Processing Magazine,2012,29(6):82-97.
[2]Ou P,Zhang Z,Lu K,et al.Object detection in remote sensing images based on convolutional neural networks[J].Laser&Optoelectronics Progress,2019,56(5):051002.欧攀,张正,路奎,等.基于卷积神经网络的遥感图像目标检测研究[J].激光与光电子学进展,2019,56(5):051002.
[3]Zhang H,Wang K F,Wang F Y.Advances and perspectives on applications of deep learning in visual object detection[J].Acta Automatica Sinica,2017,43(8):1289-1305.张慧,王坤峰,王飞跃.深度学习在目标视觉检测中的应用进展与展望[J].自动化学报,2017,43(8):1289-1305.
[4]Gu Y,Xu Y.Architecture design of deep convolutional neural network for SAR target recognition[J].Journal of Image and Graphics,2018,23(6):928-936.谷雨,徐英.面向SAR目标识别的深度卷积神经网络结构设计[J].中国图象图形学报,2018,23(6):928-936.
[5]Russakovsky O,Deng J,Su H,et al.ImageNet large scale visual recognition challenge[J].International Journal of Computer Vision,2015,115(3):211-252.
[6]Uijlings J R R,van de Sande K E A,Gevers T,et al.Selective search for object recognition[J].International Journal of Computer Vision,2013,104(2):154-171.
[7]Redmon J,Farhadi A.YOLO9000:better,faster,stronger[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2017:6517-6525.
[8]Sun H Z,Chang T Q,Wang Q D,et al.Image detection method for tank and armored targets based on hierarchical multi-scale convolution feature extraction[J].Acta Armamentarii,2017,38(9):1681-1691.孙皓泽,常天庆,王全东,等.一种基于分层多尺度卷积特征提取的坦克装甲目标图像检测方法[J].兵工学报,2017,38(9):1681-1691.
[9]Lim T R,Guntoro A T.Car recognition using Gabor filter feature extraction[C]∥Asia-Pacific Conference on Circuits and Systems,2002:451-455.
[10]Chen L L,Zhang Z D,Peng L.Real time detection method based on improved single shot multibox detector[J].Laser&Optoelectronics Progress,2019,56(1):011002.陈立里,张正道,彭力.基于改进SSD的实时检测方法[J].激光与光电子学进展,2019,56(1):011002.
[11]Girshick R.Fast R-CNN[C]∥IEEE International Conference on Computer Vision,2015:1440-1448.
[12]Ren S Q,He K M,Girshick R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149.
[13]Redmon J,Divvala S,Girshick R,et al.You only look once:unified,real-time object detection[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2016:779-788.
[14]Redmon J,Farhadi A.Yolov3:an incremental improvement[J].arXiv preprint arXiv:1804.02767,2018.
[15]He K M,Zhang X Y,Ren S Q,et al.Deep residual learning for image recognition[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2016:770-778.
[16]Szegedy C,Ioffe S,Vanhoucke V,et al.Inceptionv4,inception-Resnet and the impact of residual connections on learning[C]∥AAAI Conference on Artificial Intelligence(AAAI-17),2017:4278-4284.
[17]Wei Y M,Quan J C,Hou Y Q Y.Aerial image location of unmanned aerial vehicle based on YOLOv2[J].Laser&Optoelectronics Progress,2017,54(11):111002.魏湧明,全吉成,侯宇青阳.基于YOLO v2的无人机航拍图像定位研究[J].激光与光电子学进展,2017,54(11):111002.
[18]Hinton G,Deng L,Yu D,et al.Deep neural networks for acoustic modeling in speech recognition:the shared views of four research groups[J].IEEESignal Processing Magazine,2012,29(6):82-97.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |