基于多波段深度神经网络的舰船目标识别

考虑多波段图像的融合识别可以扩展识别系统的应用范围，本文探索并设计了一种基于卷积神经网络的融合识别方法。该方法以AlexNet网络模型为基础，同时对可见光、中波红外和长波红外三波段图像进行特征提取; 然后，利用互信息的方法对串联的三波段特征向量进行特征选择，依据重要性排序的方式选定固定长度的特征向量; 最后，依据特征提取层级的不同，分别以早期融合、中期融合和后期融合3种融合方式来验证算法的有效性。采用自建的三波段舰船图像数据库进行了模型的训练和测试，共包含6类目标，5 000余张图像。实验结果显示，采用的3种融合识别方法中，中间层融合的识别准确率最高，达到84.5%，比早期融合和后期融合分别高5%和7%左右。另外，在本文的应用场景下，无论何种融合方式，其融合识别的准确率均明显高于其他单波段识别的准确率。

Abstract

The fusion recognition of multi-band images can extend the application range of recognition systems. A fusion method based on convolutional neural networks (CNN) was explored and designed in this paper. Based on the AlexNet network model， it was extracted that the ship target features of three wave band images concurrently in visible light， Middle Wave Infrared (MWIR) and Long Wave Infrared (LWIR) bands. Then， it performed the feature selection for concatenated three-band eigenvectors by using the mutual information method and determines the dimensions of fusion eigenvectors according to sorting the importance of concatenated feature eigenvectors. Finally， three fusion methods named as Early fusion， Middle fusion and Late fusion were used to verify respectively the effectiveness of the proposed algorithm according to the features extracted from different levels. An available ship target dataset in three bands containing 6 categories of targets and more than 5 000 images was established for our experimental verification. The results show that the recognition rate from Middle fusion reaches 84.5%. Compared with Early Fusion and Late Fusion， it increases by 8% and 12%. Moreover， the recognition rates of all three fusion methods have been improve significantly as compared to that of the single band recognitions at the same application scene.

参考文献

[1] WAGNER J， FISCHER V， HERMAN M， et al.. Multispectral pedestrian detection using deep fusion convolutional neural networks ［C］. 24th European Symposium on Artificial Neural Networks， Computational Intelligence and Machine Learning (ESANN)， 2016: 509-514.

[2] NGIAM J， KHOSLA A， KIM M， et al.. Multimodal deep learning ［C］. Proceedings of the 28th international conference on machine learning (ICML-11)， 2011: 689-696.

[3] SRIVASTAVA N， SALAKHUTDINOV R R. Multimodal learning with deep boltzmann machines ［C］. Proceedings of the 25th International Conference on Neural Information Processing Systems， NIPS， 2012: 2222-2230.

[4] KARPATHY A， TODERICI G， SHETTY S， et al.. Large-scale video classification with convolutional neural networks ［C］. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition， 2014: 1725-1732.

[5] WANG LW， LI Y， LAZEBNIK S. Learning deep structure-preserving image-text embeddings ［C］. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016: 5005-5013.

[6] SOCHER R， HUVAL B， BATH B， et al.. Convolutional-recursive deep learning for 3d object classification ［C］. Proceedings of the 25th International Conference on Neural Information Processing Systems， 2012: 656-664.

[7] SIMONYAN K， ZISSERMAN A. Two-stream convolutional networks for action recognition in videos ［C］. Proceedings of the 27th International Conference on Neural Information Processing Systems， MIT Press， 2014: 568-576.

[8] GUNDOGDU E， SOLMAZ B， YCESOY V， et al.. MARVEL: A large-scale image dataset for maritime vessels ［C］. Asian Conference on Computer Vision，Springer， 2016: 165-180.

[9] BOUSETOUANE F， MORRIS B. Fast CNN surveillance pipeline for fine-grained vessel classification and detection in maritime scenarios ［C］. 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)， 2016: 242-248.

[10] REN S Q， HE K M， GIRSHICK R， et al.. Faster R-CNN: Towards real-time object detection with region proposal networks ［C］. AIPS Proceedings of the 28th International Conference on Neural Information Processing Systems， 2015: 91-99.

[11] ZHANG M M， CHOI J， DANIILIAIS K， et al.. Vais: A dataset for recognizing maritime imagery in the visible and infrared spectrums ［C］. IEEE Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops， 2015: 10-16.

[12] 周飞燕，金林鹏，董军. 卷积神经网络研究综述［J］. 计算机学报， 2017， 40(6): 1229-1251.

ZHOU F Y， JIN L P， DONG J. Review of convolutional neural network ［J］. Chinese Journal of Computers， 2017， 40(6): 1229-1251.(in Chinese)

[13] KRIZHEVSKY A， SUTSKEVER I， HINTON G E. ImageNet classification with deep convolutional neural networks ［J］. Communications of the ACM， 60(6): 84-90.

[14] NAIR V， HINTON G E. Rectified linear units improve restricted boltzmann machines ［C］ Proceedings of the 27th International Conference on Machine Learning (ICML-10)， Omnipress， 2010: 807-814.

[15] ZHANG Y， WU J X， CAI J F. Compact representation for image classification: To choose or to compress ［C］ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition， 2014: 907-914.

[16] YE P， KUMAR J， DOERMANN D. Beyond human opinion scores: blind image quality assessment based on synthetic scores ［C］ IEEE Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition， 2014: 4241-4248.

[17] XUE W F， ZHANG L， MOU X Q， et al.. Gradient magnitude similarity deviation: a highly efficient perceptual image quality index ［J］. IEEE Transactions on Image Processing， 2014， 23(2): 684-695.

[18] SHEIKH H R， BOVIK A C， DE VECIANA G. An information fidelity criterion for image quality assessment using natural scene statistics ［J］. IEEE Transactions on image Processing， 2005， 14(12): 2117-2128.

[19] ZHANG L， ZHANG L， MOU X， et al.. FSIM: a feature similarity index for image quality assessment ［J］. IEEE transactions on Image Processing， 2011， 20(8): 2378-2386.

[20] WANG Z， BOVIK A C， SHEIKH H R， et al.. Image quality assessment: from error visibility to structural similarity ［J］. IEEE Transactions on Image Processing， 2004， 13(4): 600-612.

刘峰, 沈同圣, 马新星, 张健. 基于多波段深度神经网络的舰船目标识别[J]. 光学精密工程, 2017, 25(11): 2939. LIU Feng, SHEN Tong-sheng, MA Xin-xing, ZHANG Jian. Ship recognition based on multi-band deep neural network[J]. Optics and Precision Engineering, 2017, 25(11): 2939.

基于多波段深度神经网络的舰船目标识别

关于本站 Cookie 的使用提示

全站搜索

基于多波段深度神经网络的舰船目标识别

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索