基于注意力与特征融合的光学遥感图像飞机目标检测

兰旭婷; 郭中华; 李昌昊

doi:10.37188/CJLCD.2021-0088

您当前的位置：

首页 >

文章列表页 >

基于注意力与特征融合的光学遥感图像飞机目标检测

图像增强与分割 | 更新时间：2021-11-08

- 基于注意力与特征融合的光学遥感图像飞机目标检测
- Attention and feature fusion for aircraft target detection in optical remote sensing images
- 液晶与显示 2021年36卷第11期页码：1506-1515
- 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：
  
  [ " 兰旭婷(1995-)，女，宁夏银川人，硕士研究生，2019年于西北民族大学获得学士学位，主要从事深度学习与目标检测方面的研究。E-mail：1742192370@qq.com " ]
  [ " 郭中华(1973-)，男，宁夏银川人，博士，教授，2010年于西北工业大学获得博士学位，主要从事图像处理、机器视觉方面的研究。E-mail：guozhh@nxu.edu.cn " ]
- 基金信息：
  
  宁夏自然科学基金(2020AAC03026);宁夏大学研究生创新研究项目(GIP2020075)
- DOI：10.37188/CJLCD.2021-0088
  中图分类号： TP391.41
- 收稿日期：2021-04-02，
  
  修回日期：2021-04-29，
  
  纸质出版日期：2021-11
- 稿件说明：
移动端阅览
兰旭婷, 郭中华, 李昌昊. 基于注意力与特征融合的光学遥感图像飞机目标检测[J]. 液晶与显示, 2021,36(11):1506-1515.

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.
兰旭婷, 郭中华, 李昌昊. 基于注意力与特征融合的光学遥感图像飞机目标检测[J]. 液晶与显示, 2021,36(11):1506-1515. DOI： 10.37188/CJLCD.2021-0088.

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 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

改进SSD的飞机遥感图像目标检测

基于Double-Head的雾天图像目标检测

跨尺度自适应融合的车辆与行人检测算法