Ship detection in remote sensing image based on dense RFB and LSTM
- Vol. 26, Issue 9, Pages: 1859-1871(2022)
Published: 07 September 2022
DOI: 10.11834/jrs.20211042
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Published: 07 September 2022 ,
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张涛,杨小冈,卢孝强,卢瑞涛,张胜修.2022.Dense RFB和LSTM遥感图像舰船目标检测.遥感学报,26(9): 1859-1871
Zhang T,Yang X G,Lu X Q,Lu R T and Zhang S X. 2022. Ship detection in remote sensing image based on dense RFB and LSTM. National Remote Sensing Bulletin,26(9):1859-1871
针对当前遥感图像舰船目标检测精度不佳问题,本文构建舰船目标数据集STAR,提出基于Dense RFB和LSTM多尺度舰船目标检测算法。该算法首先在SSD网络基础上设计了浅层特征增强模块,基于人眼视点图采用Dense RFB特征复用和膨胀卷积增大感受野的尺度和种类,增强浅层网络对细节特征的提取能力;其次设计了深层多尺度特征金字塔融合模块,采用FPN和LSTM思想,基于反卷积和残差网络对深层不同尺度特征进行融合,增强网络结构非线性和特征层的表征能力;最后加入聚焦分类损失函数进行联合训练,有效避免了正负样本失衡问题。在遥感图像舰船数据集上实验,本文所提舰船目标检测算法精度均值达到
<math id="M1"><mn mathvariant="normal">81.98</mn><mi mathvariant="normal">%</mi></math>
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9.82133293
2.37066650
,检测速度达到29.6帧/s。此外,遥感图像中成像模糊、被遮挡、部分被裁剪等舰船目标的检测效果也优于原有经典算法,实验结果表明该算法对遥感图像舰船目标检测的泛化能力较强,有效地提高了遥感图像舰船目标检测的精度。
Ship detection plays a crucial role in various applications and has drawn increasing attention in recent years. Deep learning methods based on CNNs
particularly SSD
have greatly improved detection performance due to their highly efficient feature extraction capability. However
SSD still has two problems. For instance
the detection network of arbitrarily arranged ship targets lacks a connection between high and low-level features and ignores contextual semantic information. Another problem is that natural factors such as light and clouds affect remote sensing images
thus ship detection may cause an imbalance of positive and negative samples.
Aiming at solving the above issues
this paper proposes to achieve ship detection in remote sensing images by using a method based on Dense RFB and LSTM. This proposed method includes three elements. First
to enhance the detail feature extraction capability
this proposed method introduces a shallow feature enhancement module. This module draws on the idea of the human viewpoint
which uses Dense RFB feature reuse and expansion convolution to increase the receptive field. Second
to effectively extract deep semantic information and enhance the expressive ability of the network feature layer
a deep multi-scale feature pyramid fusion module (MFPF) is designed
as this proposed method draws on FPN and LSTM deconvolution and residual structure fuse deep multi-scale features. Finally
to solve the imbalance of positive and negative samples
the focal classification loss function is added
improving the accuracy of ship detection during training.
The experiments were carried out on an optical remote sensing image dataset
in which only the ship dataset was used for training
validation
and testing. Results indicate that the proposed algorithm achieved an Average Precision (AP) of 81.98% and the detection speed reached 29.6
fps
for ship targets
in which most ships were detected successfully. Moreover
for blurred
occluded
and partially-cropped ship targets
the algorithm’s detection effect is better than the traditional algorithm. Qualitative and quantitative results indicate that the generalization capability of the proposed method enhances ship detection.
From this paper
we can draw three conclusions: (1) The proposed method can improve the extraction of detailed features and increase the receptive fields. (2) The focal loss function method shows good generalization capability. (3) The rotating box detection method is suitable for multi-scale and densely-arranged remote sensing images.
舰船目标检测Dense RFB特征金字塔LSTM多尺度特征
ship target detectionDense RFBfeature pyramid networksLSTMmulti-scale feature
Cao G M, Xie X M, Yang W Z, Liao Q, Shi G M and Wu J J. 2018. Feature-fused SSD: fast detection for small objects//Proceedings of SPIE 10615, Ninth International Conference on Graphic and Image Processing. Qingdao: SPIE: 106151E [DOI: 10.1117/12.2304811http://dx.doi.org/10.1117/12.2304811]
Dong C. 2020. Research on the Detection of Ship Targets on the Sea Surface in Optical Remote Sensing Image. Changchun: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science, China (董超. 2020. 可见光遥感图像海面舰船目标检测技术研究. 长春: 中国科学院大学(中国科学院长春光学精密机械与物理研究所) [DOI: 10.27522/d.cnki.gkcgs.2020.000037http://dx.doi.org/10.27522/d.cnki.gkcgs.2020.000037]
Guo W. 2019. Automatic Ship Detection in Optical Remote Sensing Images Based on Deep Learning. Wuhan: Wuhan University: 15-20
郭威. 2019. 基于深度学习的光学遥感图像自动舰船检测. 武汉: 武汉大学: 15-20
He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE: 770-778. [DOI: 10.1109/CVPR.2016.90http://dx.doi.org/10.1109/CVPR.2016.90]
Krizhevsky A, Sutskever I and Hinton G E. 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6): 84-90. [DOI: 10.1145/3065386http://dx.doi.org/10.1145/3065386]
Li H H, Zhou K P and Han T C. 2020. Ship object detection based on SSD improved with CReLU and FPN. Chinese Journal of Scientific Instrument, 41(4): 183-190
李晖晖, 周康鹏, 韩太初. 2020. 基于CReLU和FPN改进的SSD舰船目标检测. 仪器仪表学报, 41(4): 183-190 [DOI: 10.19650/j.cnki.cjsi.J2006122http://dx.doi.org/10.19650/j.cnki.cjsi.J2006122]
Lin T Y, Dollár P, Girshick R, He K M, Hariharan B, Belongie S. 2017. Feature pyramid networks for object detection//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE: 936-944. [DOI: 10.1109/CVPR.2017.106http://dx.doi.org/10.1109/CVPR.2017.106]
Lin T Y, Goyal P, Girshick R, He K M and Dollár P. 2020. Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2): 318-327. [DOI: 10.1109/TPAMI.2018.2858826http://dx.doi.org/10.1109/TPAMI.2018.2858826]
Liu S T, Huang D and Wang Y H. 2018. Receptive field block net for accurate and fast object detection//15th European Conference on Computer Vision. Munich: Springer: 385-400. [DOI: 10.1007/978-3-030-01252-6_24http://dx.doi.org/10.1007/978-3-030-01252-6_24]
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y and Berg A C. 2016. SSD: single shot MultiBox detector//14th European Conference on Computer Vision. Amsterdam: Springer: 21-27 [DOI: 10.1007/978-3-319-46448-0_2http://dx.doi.org/10.1007/978-3-319-46448-0_2]
Luo H L and Chen H K. 2020. Survey of object detection based on deep learning. Acta Electronica Sinica, 48(6): 1230-1239
罗会兰, 陈鸿坤. 2020. 基于深度学习的目标检测研究综述. 电子学报, 48(6): 1230-1239 [DOI: 10.3969/j.issn.0372-2112.2020.06.026http://dx.doi.org/10.3969/j.issn.0372-2112.2020.06.026]
Ma J, Shi W X, Bao S L. 2019. Ship target detection in remote sensing images based on feature fusion SSD. Journal of Computer Applications, 39(S2): 253-256
马健, 史文旭, 鲍胜利. 2019. 基于特征融合SSD的遥感图像舰船目标检测. 计算机应用, 39(S2): 253-256 [DOI: CNKI:SUN:JSJY.0.2019-S2-050http://dx.doi.org/CNKI:SUN:JSJY.0.2019-S2-050]
Redmon J, Divvala S, Girshick R and Farhadi A. 2016. You only look once: unified, real-time object detection//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE: 779-788. [DOI: 10.1109/CVPR.2016.91http://dx.doi.org/10.1109/CVPR.2016.91]
Ren S Q, He K M, Girshick R and Sun J. 2017. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6): 1137-1149. [DOI: 10.1109/TPAMI.2016.2577031http://dx.doi.org/10.1109/TPAMI.2016.2577031]
Shi W X, Jiang J H and Bao S L. 2020a. Ship detection method in remote sensing image based on feature fusion. Acta Photonica Sinica, 49(7): 57-67
史文旭, 江金洪, 鲍胜利. 2020. 基于特征融合的遥感图像舰船目标检测方法. 光子学报, 49(7): 57-67 [DOI: 10.3788/gzxb20204907.0710004http://dx.doi.org/10.3788/gzxb20204907.0710004]
Shi W X, Tan D L and Bao S L. 2020b. Feature enhancement SSD algorithm and its application in remote sensing images target detection. Acta Photonica Sinica, 49(1): 154-163
史文旭, 谭代伦, 鲍胜利. 2020. 特征增强SSD算法及其在遥感目标检测中的应用. 光子学报, 49(1): 154-163 [DOI: 10.3788/gzxb20204901.0128002http://dx.doi.org/10.3788/gzxb20204901.0128002]
Wang L W, Feng Y Q and Zhang M B. 2019. Target detection method for optical remote sensing imagery. Systems Engineering and Electronics, 41(10): 2163-2169
王伦文, 冯彦卿, 张孟伯. 2019. 光学遥感图像目标检测方法. 系统工程与电子技术, 41(10): 2163-2169 [DOI: 10.3969/j.issn.1001-506X.2019.10.02http://dx.doi.org/10.3969/j.issn.1001-506X.2019.10.02]
Wang X K, Jiang H X and Lin K Y. 2020. Remote sensing image ship detection based on modified YOLO algorithm. Journal of Beijing University of Aeronautics and Astronautics, 46(6): 1184-1191
王玺坤, 姜宏旭, 林珂玉. 2020. 基于改进型YOLO算法的遥感图像舰船检测. 北京航空航天大学学报, 46(6): 1184-1191 [DOI: 10.13700/j.bh.1001-5965.2019.0394http://dx.doi.org/10.13700/j.bh.1001-5965.2019.0394]
Xie X L, Li C X, Yang X G, Xi J X and Chen T. 2020. Dynamic receptive field-based object detection in aerial imaging. Acta Optica Sinica, 40(4): 107-119
谢学立, 李传祥, 杨小冈, 席建祥, 陈彤. 2020. 基于动态感受野的航拍图像目标检测算法. 光学学报, 40(4): 107-119 [DOI: 10.3788/AOS202040.0415001http://dx.doi.org/10.3788/AOS202040.0415001]
Yu Y, Ai H, He X J, Yu S H, Zhong X and Zhu R F. 2020. Attention-based feature pyramid networks for ship detection of optical remote sensing image. Journal of Remote Sensing, 24(2): 107-115
于野, 艾华, 贺小军, 于树海, 钟兴, 朱瑞飞. 2020. A-FPN算法及其在遥感图像船舶检测中的应用. 遥感学报, 24(2): 107-115 [DOI: 10.11834/jrs.20208264http://dx.doi.org/10.11834/jrs.20208264]
Zeiler M D and Fergus R. 2014. Visualizing and understanding convolutional networks//Proceedings of the 13th 2014 European Conference on Computer Vision. Zurich: Springer: 818-833. [DOI: 10.1007/978-3-319-10590-1_53http://dx.doi.org/10.1007/978-3-319-10590-1_53]
Zhang C G, Xiong B L, Kuang G Y. 2020. A survey of ship detection in optical satellite remote sensing images. Chinese Journal of Radio Science, 35(5): 637-647
张财广, 熊博莅, 匡纲要. 2020. 光学卫星遥感图像舰船目标检测综述. 电波科学学报, 35(5): 637-647 [DOI: 10.13443/j.cjors.2020040603http://dx.doi.org/10.13443/j.cjors.2020040603]
Zhang M T. 2019. The Research on Small Object Detection Algorithm in Aerial Images Based on Depth Neural Network. Xi’an: Xidian University
张敏桐. 2019. 基于深度神经网络的航拍图像小目标检测算法研究. 西安: 西安电子科技大学 [DOI: 10.27389/d.cnki.gxadu.2019.002277http://dx.doi.org/10.27389/d.cnki.gxadu.2019.002277]
Zhang W and Wei J J. 2020. Improved YOLO v3 fire detection algorithm embedded in DenseNet structure and dilated convolution module. Journal of Tianjin University (Science and Technology), 53(9): 976-983
张为, 魏晶晶. 2020. 嵌入DenseNet结构和空洞卷积模块的改进YOLO v3火灾检测算法. 天津大学学报(自然科学与工程技术版), 53(9): 976-983 [DOI: 10.11784/tdxbz201907079http://dx.doi.org/10.11784/tdxbz201907079]
Zhou H, Guo J, Zhu C R and Wang R S. 2010. Ship detection from optical remote sensing images based on PLSA model. Journal of Remote Sensing, 14(4): 663-680
周晖, 郭军, 朱长仁, 王润生. 2010. 引入PLSA模型的光学遥感图像舰船检测. 遥感学报, 14(4): 663-680 [DOI: 10.11834/jrs.20100404http://dx.doi.org/10.11834/jrs.20100404]
Zhu L Y, Xiong G, Guo D M and Yu W X. 2019. Ship target detection and segmentation method based on multi-fractal analysis. The Journal of Engineering, (21): 7876-7879. [DOI: 10.1049/joe.2019.0764http://dx.doi.org/10.1049/joe.2019.0764]
Zhu M. 2004. Recall, precision and average precision. Department of Statisticsand Actuarial Science, University of Waterloo, Waterloo 2 (2004) 30.32.
Zhu Y, Fang G S, Zheng B B and Han F. 2020. Research on detection method of refined rotated boxes in remote sensing. Acta Photonica Sinica, 45(1): 1-11
朱煜, 方观寿, 郑兵兵, 韩飞. 2020. 基于旋转框精细定位的遥感目标检测方法研究. 自动化学报, 45(1): 1-11 [DOI: 10.16383/j.aas.c200261http://dx.doi.org/10.16383/j.aas.c200261]
Zou Z, Shi Z. 2017. Random access memories: A new paradigm for target detection in high resolution aerial remote sensing images. IEEE Transactions on Image Processing, 2017, 27(3): 1100-1111. [DOI: 10.1109/TIP.2017.2773199http://dx.doi.org/10.1109/TIP.2017.2773199]
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