Attention and feature fusion for aircraft target detection in optical remote sensing images

Xu-ting LAN; Zhong-hua GUO; Chang-hao LI

doi:10.37188/CJLCD.2021-0088

您当前的位置：

首页 >

文章列表页 >

Attention and feature fusion for aircraft target detection in optical remote sensing images

Target Detectionand Tracking | 更新时间：2021-11-08

- Attention and feature fusion for aircraft target detection in optical remote sensing images
- Chinese Journal of Liquid Crystals and Displays Vol. 36, Issue 11, Pages: 1506-1515(2021)
- 作者机构：
  
  1. 宁夏大学物理与电子电气工程学院, 宁夏银川 750021
  2. 宁夏沙漠信息智能感知重点实验室, 宁夏银川 750021
- 作者简介：
- 基金信息：
  
  Natural Science Foundation of Ningxia Hui Autonomous Region(2020AAC03026)
- DOI：10.37188/CJLCD.2021-0088
  CLC： TP391.41
- Received：02 April 2021，
  
  Revised：29 April 2021，
  
  Published：2021-11
- 稿件说明：
移动端阅览
Xu-ting LAN, Zhong-hua GUO, Chang-hao LI. Attention and feature fusion for aircraft target detection in optical remote sensing images[J]. Chinese journal of liquid crystals and displays, 2021, 36(11): 1506-1515.
DOI：

Xu-ting LAN, Zhong-hua GUO, Chang-hao LI. Attention and feature fusion for aircraft target detection in optical remote sensing images[J]. Chinese journal of liquid crystals and displays, 2021, 36(11): 1506-1515. DOI： 10.37188/CJLCD.2021-0088.

摘要

光学遥感图像受背景复杂度和语义信息量大等影响，在检测精度和效率上仍然存在一定的不足。本文提出了以Resnet50为基础特征提取的SSD300网络模型，加入注意力机制CBAM模块和特征融合FPN模块，采用Soft-NMS策略选取最终预测框，对遥感图像飞机目标进行更加有效地检测。最后，在2 150张飞机遥感图像数据集上进行训练，当IoU为0.5和0.75时平均精度MAP达到92.54%和63.44%，较改进前的算法模型提升了5.04%和11.38%，检测速度达到24.1 FPS。实验结果表明，该方法可以有效提高物体的检测能力，以及快速、准确地检测机场区域内的飞机物体，有效降低了飞机物体的漏检率，提高了检测精度和速度。

Abstract

Optical remote sensing images are affected by the complexity of the background and the large amount of semantic information

and there are still certain deficiencies in detection accuracy and efficiency. This paper proposes an SSD300 network model based on Resnet50 for feature extraction

adding the attention mechanism CBAM module and feature fusion FPN module

and adopting the Soft-NMS strategy to select the final prediction frame to detect aircraft targets in remote sensing images more effectively. Finally

training on 2 150 aircraft remote sensing image data sets

when the IoU is 0.5 and 0.75

the average accuracy MAP reaches 92.54% and 63.44%

which are 5.04% and 11.38% higher than the previous algorithm model

and the detection speed reaches 13.4 FPS. Experimental results show that this method can effectively improve the detection ability of objects and quickly and accurately detect aircraft objects in the airport area

effectively reducing the missed detection rate of aircraft objects and improving detection accuracy and speed.

关键词

Keywords

references

祝文韬 , 谢宝蓉 , 王琰 , 等 . 光学遥感图像中的飞机目标检测技术研究综述 . 计算机科学 , 2020 . 47 ( S2 ): 165 - 171, 182 . https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA2020S2029.htm https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA2020S2029.htm .

W T ZHU , B R XIE , Y WANG , 等 . A review of aircraft target detection technology in optical remote sensing images . Computer Science , 2020 . 47 ( S2 ): 165 - 171, 182 . https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA2020S2029.htm https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA2020S2029.htm .

李文斌, 何冉. 基于FCN与CNN的遥感影像飞机目标检测方法[J/OL]. 计算机工程: 1-11[2021-02-01] . https://www.doc88.com/p-71161845887317.html https://www.doc88.com/p-71161845887317.html .

LI W B, HE R. Aircraft detection models of remote sensing images based on FCN and CNN[J/OL]. Computer Engineering: 1-11[2021-02-01] . https://www.doc88.com/p-71161845887317.html https://www.doc88.com/p-71161845887317.html . (in Chinese)

G CHENG , J W HAN , L GUO , 等 . Object detection in remote sensing imagery using a discriminatively trained mixture model . ISPRS Journal of Photogrammetry and Remote Sensing , 2013 . 85 32 - 43 . DOI: 10.1016/j.isprsjprs.2013.08.001 http://doi.org/10.1016/j.isprsjprs.2013.08.001 .

G LIU , X SUN , K FU , 等 . Aircraft recognition in high-resolution satellite images using coarse-to-fine shape prior . IEEE Geoscience and Remote Sensing Letters , 2013 . 10 ( 3 ): 573 - 577 . DOI: 10.1109/LGRS.2012.2214022 http://doi.org/10.1109/LGRS.2012.2214022 .

ARANDJELOVI O. Object matching using boundary descriptors[C]// Proceedings of the British Machine Vision Conference . Surrey, UK: BMVA Press, 2012.

S M M KAHAKI , M J NORDIN , A H ASHTARI , 等 . Deformation invariant image matching based on dissimilarity of spatial features . Neurocomputing , 2016 . 175 1009 - 1018 . DOI: 10.1016/j.neucom.2015.09.106 http://doi.org/10.1016/j.neucom.2015.09.106 .

GIRSHICK R, DONAHUE J, DARRELL T, et al . Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition . Columbus: IEEE, 2014: 580-587.

GIRSHICK R. Fast R-CNN[C]// Proceedings of 2015 IEEE International Conference on Computer Vision . Santiago: IEEE, 2015: 1440-1448.

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

REDMON J, DIVVALA S, GIRSHICK R, et al . You only look once: unified, real-time object detection[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas: IEEE, 2016: 779-788.

REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu: IEEE, 2017: 7263-7271.

REDMON J, FARHADI A. YOLOv3: an incremental improvement[J]. arXiv : 1804.02767, 2018.

LIU W, ANGUELOV D, ERHAN D, et al . SSD: single shot MultiBox detector[C]// Proceedings of the 14 th European Conference on Computer Vision . Amsterdam, The Netherlands: Springer, 2016: 21-37.

董潇潇 , 何小海 , 吴晓红 , 等 . 基于注意力掩模融合的目标检测算法 . 液晶与显示 , 2019 . 34 ( 8 ): 825 - 833 . http://cjlcd.lightpublishing.cn/thesisDetails#10.3788/YJYXS20193408.0825 http://cjlcd.lightpublishing.cn/thesisDetails#10.3788/YJYXS20193408.0825 .

X X DONG , X H HE , X H WU , 等 . Object detection algorithm based on attention mask fusion . Chinese Journal of Liquid Crystals and Displays , 2019 . 34 ( 8 ): 825 - 833 . http://cjlcd.lightpublishing.cn/thesisDetails#10.3788/YJYXS20193408.0825 http://cjlcd.lightpublishing.cn/thesisDetails#10.3788/YJYXS20193408.0825 .

HE K M, ZHANG X Y, REN S Q, et al . Deep residual learning for image recognition[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas: IEEE, 2016: 770-778.

SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. [2019-06-01] . https://arxiv.org/pdf/1409.1556.pdf https://arxiv.org/pdf/1409.1556.pdf .

WOO S, PARK J, LEE J Y, et al . CBAM: convolutional block attention module[C]// Proceedings of the 15 th European Conference on Computer Vision . Munich, Germany: Springer, 2018: 3-19.

S Q REN , K M HE , R GIRSHICK , 等 . Faster R-CNN: towards real-time object detection with region proposal networks . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 . 39 ( 6 ): 1137 - 1149 . DOI: 10.1109/TPAMI.2016.2577031 http://doi.org/10.1109/TPAMI.2016.2577031 .

BODLA N, SINGH B, CHELLAPPA R, et al . Soft-NMS-improving object detection with one line of code[C]// Proceedings of 2017 IEEE International Conference on Computer Vision . Venice: IEEE, 2017.

M EVERINGHAM , S M A ESLAMI , L VAN GOOL , 等 . The PASCAL visual object classes challenge: a retrospective . International Journal of Computer Vision , 2015 . 111 ( 1 ): 98 - 136 . DOI: 10.1007/s11263-014-0733-5 http://doi.org/10.1007/s11263-014-0733-5 .

IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[DB/OL]. (2015-05-02)[2019-12-01] . https://arxiv.org/abs/1502.03167 https://arxiv.org/abs/1502.03167 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Improved SSD based aircraft remote sensing image target detection

Object detection in foggy image based on Double-Head

Attention and cross-scale fusion for vehicle and pedestrian detection

Related Author

Xu-ting LAN

Zhong-hua GUO

Hao-tong WANG

Chang-hao LI

Hao-tong WANG

LI Ren-si

SHI Yun-yu

Related Institution

School of Physics and Electronic and Electrical Engineering， Ningxia University

Department of Electric and Electronic Engineering， Shanghai University of Engineering Science

College of Software， Liaoning Technical University

College of Mining， Liaoning Technical University

School of Computer Science and Technology， Shandong University of Technology

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China 130033 Postal code：130033
Tel：0431-86176059 Email：yjxs@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰