基于轻量级网络的装甲目标快速检测
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摘要
针对战场环境下装甲目标的检测任务,提出一种基于轻量级网络的快速检测方法.首先以轻量级卷积神经网络MobileNet作为骨架网络,构建一个多尺度的单步检测网络;然后针对装甲目标的尺寸分布情况使用分辨率更高的卷积特征图,并在每个检测单元上新加入一个残差模块,增强了对小尺度目标的检测能力;最后引入focal-loss损失来替代传统的交叉熵损失函数,有效地克服了训练过程中存在的正负样本分布极度不平衡的问题.针对装甲目标构建了专用的目标检测数据集,并在该数据集上对几种目前主流的单步检测方法进行了训练和测试,实验结果表明,该方法在检测精度、模型容量以及运行速度上均取得了较好的效果,对于无人机等小型移动侦查平台具备良好的适用性.
Focused on the detection task of armored target in battlefield environment, a fast detection method based on lightweight convolutional neural network is proposed in this paper. Firstly, based on the lightweight backbone network(MobileNet), a multi-scale single-stage detection framework is developed. Secondly, considering the size distribution of armored target, higher resolution feature maps are selected and a new designed Resblock is added to each detection unit to enhance the detection performance for small targets. At last, focal-loss function is introduced to replace the traditional cross entropy loss function, which effectively overcomes the extreme imbalance of the distribution of the positive and negative samples in training processes. A special detection dataset for armored target is constructed, based on which the comparable experiments with state-of-art detection methods are conducted. Experimental results show that the proposed method achieves good performance in detection accuracy, model size and operation speed, and is especially suitable for small mobile reconnaissance platforms such as UAVs(unmanned aerial vehicle).
Focused on the detection task of armored target in battlefield environment, a fast detection method based on lightweight convolutional neural network is proposed in this paper. Firstly, based on the lightweight backbone network(MobileNet), a multi-scale single-stage detection framework is developed. Secondly, considering the size distribution of armored target, higher resolution feature maps are selected and a new designed Resblock is added to each detection unit to enhance the detection performance for small targets. At last, focal-loss function is introduced to replace the traditional cross entropy loss function, which effectively overcomes the extreme imbalance of the distribution of the positive and negative samples in training processes. A special detection dataset for armored target is constructed, based on which the comparable experiments with state-of-art detection methods are conducted. Experimental results show that the proposed method achieves good performance in detection accuracy, model size and operation speed, and is especially suitable for small mobile reconnaissance platforms such as UAVs(unmanned aerial vehicle).
引文
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(1)https://github.com/tzutalin/labelImg
[2]Sun Haoze,Chang Tianqing,Wang Quandong,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(in Chinese)(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)
[3]Yin Hongpeng,Chen Bo,Chai Yi,et al.Vision-based object detection and tracking[J].Acta Automatica Sinica,2016,42(10):1466-1489(in Chinese)(尹宏鹏,陈波,柴毅,等.基于视觉的目标检测与跟踪综述[J].自动化学报,2016,42(10):1466-1489)
[4]Krizhevsky A,Sutskever I,Hinton G E.ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems.Lake Tahoe:Curran Associates Inc,2012,1:1097-1105
[5]Deng J,Dong W,Socher R,et al.ImageNet:a large-scale hierarchical image database[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2009:248-255
[6]Girshick R,Donahue J,Darrell T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2014:580-587
[7]He K M,Zhang X Y,Ren S Q,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916
[8]Girshick R.Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision.Los Alamitos:IEEEComputer Society Press,2015:1440-1448
[9]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
[10]Redmon J,Divvala S,Girshick R,et al.You only look once:unified,real-time object detection[C]//Proceedings of the IEEEConference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2016:779-788
[11]Liu W,Anguelov D,Erhan D,et al.SSD:single shot multiBox detector[C]//Proceedings of the European Conference on Computer Vision.Heidelberg:Springer,2016:21-37
[12]Redmon J,Farhadi A.YOLO9000:better,faster,stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2017:6517-6525
[13]Fu C Y,Liu W,Ranga A,et al.DSSD:deconvolutional single shot detector[OL].[2018-08-06].http://arxiv.org/abs/1701.06659
[14]Everingham M,van Gool L,Williams C K I,et al.The pascal visual object classes(VOC)challenge[J].International Journal of Computer Vision,2010,88(2):303-338
[15]Geiger A,Lenz P,Urtasun R.Are we ready for autonomous driving?The Kitti vision benchmark suite[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2012:3354-3361
[16]Simonyan K,Zisserman A.Very deep convolutional networks for large-scale image recognition[OL].[2018-08-06].https://arxiv.org/abs/1409.1556
[17]He K M,Zhang X Y,Ren S Q,et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEEComputer Society Press,2016:770-778
[18]Lin T Y,Goyal P,Girshick R,et al.Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision.Los Alamitos:IEEE Computer Society Press,2017:2999-3007
[19]Iandola F N,Han S,Moskewicz M W,et al.Squeezenet:Alexnet-level accuracy with 50x fewer parameters and<0.5 MBmodel size[OL].[2018-08-06].http://arxiv.orb/abs/1602.07360
[20]Howard A G,Zhu M L,Chen B,et al.MobileNets:efficient convolutional neural networks for mobile vision applications[OL].[2018-08-06].http://arxiv.orb/abs/1704.04861
[21]Zhang X Y,Zhou X Y,Lin M X,et al.ShuffleNet:an extremely efficient convolutional neural network for mobile devices[OL].[2018-08-06].http://arxiv.orb/abs/1707.01083
[22]Lin T Y,Dollár P,Girshick R,et al.Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2017:936-944
[23]Cai Z W,Fan Q F,Feris R S,et al.A unified multi-scale deep convolutional neural network for fast object detection[C]//Proceedings of the European Conference on Computer Vision.Heidelberg:Springer,2016:354-370
(1)https://github.com/tzutalin/labelImg
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