光学学报, 2020, 40 (1): 0111018, 网络出版: 2020-01-06   

基于轻量级残差网络的红外遥感船只检测 下载: 1655次

Infrared-Remote-Sensing Ship Detection Based on Lightweight Residual Network
朱天佑 1,2,3黄凌锋 1,2,3董峰 1,2龚惠兴 1,2,*
作者单位
1 中国科学院红外探测与成像技术重点实验室, 上海 200083
2 中国科学院上海技术物理研究所, 上海 200083
3 中国科学院大学, 北京 100049
引用该论文

朱天佑, 黄凌锋, 董峰, 龚惠兴. 基于轻量级残差网络的红外遥感船只检测[J]. 光学学报, 2020, 40(1): 0111018.

Tianyou Zhu, Lingfeng Huang, Feng Dong, Huixing Gong. Infrared-Remote-Sensing Ship Detection Based on Lightweight Residual Network[J]. Acta Optica Sinica, 2020, 40(1): 0111018.

参考文献

[1] 鲍松泽, 钟兴, 朱瑞飞, 等. 基于短波红外遥感影像的船只自动检测方法[J]. 光学学报, 2018, 38(5): 0528001.

    Bao S Z, Zhong X, Zhu R F, et al. Automatic detection method of ships based on shortwave infrared remote sensing images[J]. Acta Optica Sinica, 2018, 38(5): 0528001.

[2] 王文秀, 傅雨田, 董峰, 等. 基于深度卷积神经网络的红外船只目标检测方法[J]. 光学学报, 2018, 38(7): 0712006.

    Wang W X, Fu Y T, Dong F, et al. Infrared ship target detection method based on deep convolution neural network[J]. Acta Optica Sinica, 2018, 38(7): 0712006.

[3] Jiang B T, Ma X F, Lu Y, et al. Ship detection in spaceborne infrared images based on convolutional neural networks and synthetic targets[J]. Infrared Physics & Technology, 2019, 97: 229-234.

[4] 刘峰, 沈同圣, 马新星. 特征融合的卷积神经网络多波段舰船目标识别[J]. 光学学报, 2017, 37(10): 1015002.

    Liu F, Shen T S, Ma X X. Convolutional neural network based multi-band ship target recognition with feature fusion[J]. Acta Optica Sinica, 2017, 37(10): 1015002.

[5] Li Q P, Mou L C, Liu Q J, et al. HSF-Net: multiscale deep feature embedding for ship detection in optical remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(12): 7147-7161.

[6] Cheng G, Zhou P C, Han J W. Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(12): 7405-7415.

[7] RonnebergerO, FischerP, BroxT. U-Net: convolutional networks for biomedical image segmentation[M] //Navab N, Hornegger J, Wells W, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science. Cham: Springer, 2015, 9351: 234- 241.

[8] Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481-2495.

[9] CourbariauxM, BengioY, David JP. BinaryConnect: training deep neural networks with binary weights during propagations[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems, December 7-12, 2015, Montreal, Canada. Canada: NIPS, 2015: 3123- 3131.

[10] HubaraI, CourbariauxM, SoudryD, et al. Binarized neural networks[C]//Advances in Neural Information Processing Systems 29 (NIPS 2016), December 5-10, 2016, Barcelona, Spain. Canada: NIPS, 2016: 4107- 4115.

[11] Li FF, ZhangB, Liu B. Ternary weight networks[J/OL]. ( 2016-11-19)[2019-07-25]. site/abs/1605. 04711. https://arxiv.gg363.

[12] Zhang XY, Zhou XY, Lin MX, et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 6848- 6856.

[13] Howard AG, Zhu ML, ChenB, et al. ( 2017-04-17)[2019-07-25]. site/abs/1704. 04861. https://arxiv.gg363.

[14] Romera E, Alvarez J M, Bergasa L M, et al. ERFNet: efficient residual factorized ConvNet for real-time semantic segmentation[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(1): 263-272.

[15] GlorotX, BordesA, BengioY. Deep sparse rectifier neural networks[C]//Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, April 11-13, 2011, Fort Lauderdale, USA.USA: MIT Press, 2011: 315- 323.

[16] He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 770- 778.

[17] Brownlee J. Tactics to combat imbalanced classes in your machine learning dataset[DB/OL]//Machine learning mastery, 2015-08-19[2019-07-25]. https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/.

朱天佑, 黄凌锋, 董峰, 龚惠兴. 基于轻量级残差网络的红外遥感船只检测[J]. 光学学报, 2020, 40(1): 0111018. Tianyou Zhu, Lingfeng Huang, Feng Dong, Huixing Gong. Infrared-Remote-Sensing Ship Detection Based on Lightweight Residual Network[J]. Acta Optica Sinica, 2020, 40(1): 0111018.

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