基于深度学习的航空对地小目标检测

梁华; 宋玉龙; 钱锋; 宋策

doi:10.3788/YJYXS20183309.0793

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基于深度学习的航空对地小目标检测

图像处理 | 更新时间：2020-07-06

- 基于深度学习的航空对地小目标检测
- Detection of small target in aerial photography based on deep learning
- 液晶与显示 2018年33卷第9期页码：793-800
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
  2.中国科学院大学, 北京 100049
- 作者简介：
  
  [ "梁华(1993-), 男, 四川宜宾人, 硕士研究生, 2016年于重庆大学获得学士学位, 主要从事机器学习与图像检测方面的研究。E-mail:lianghua_ucas@foxmail.com" ]
  [ "宋玉龙(1980-), 男, 吉林长春人, 博士, 硕士生导师, 2007年于中国科学院研究生院获得博士学位, 主要从事航空光电成像技术的研究。E-mail:SongYL@ciomp.ac.cn" ]
- 基金信息：
  
  国家自然科学基金(61705225)
- DOI：10.3788/YJYXS20183309.0793
  中图分类号： TP391
- 收稿日期：2018-04-02，
  
  录用日期：2018-6-8，
  
  纸质出版日期：2018-09-05
- 稿件说明：
移动端阅览
梁华, 宋玉龙, 钱锋, 等. 基于深度学习的航空对地小目标检测[J]. 液晶与显示, 2018,33(9):793-800.

Hua LIANG, Yu-long SONG, Feng QIAN, et al. Detection of small target in aerial photography based on deep learning[J]. Chinese journal of liquid crystals and displays, 2018, 33(9): 793-800.
梁华, 宋玉龙, 钱锋, 等. 基于深度学习的航空对地小目标检测[J]. 液晶与显示, 2018,33(9):793-800. DOI： 10.3788/YJYXS20183309.0793.

Hua LIANG, Yu-long SONG, Feng QIAN, et al. Detection of small target in aerial photography based on deep learning[J]. Chinese journal of liquid crystals and displays, 2018, 33(9): 793-800. DOI： 10.3788/YJYXS20183309.0793.

摘要

针对航拍图像中对地小目标识别率低、定位效果差的问题，提出了一种基于深度学习的目标检测算法。该算法利用VGG16网络作为微调网络，并添加部分深层网络，通过提取目标浅层特征与深层特征进行联合训练，克服检测过程中定位与识别相互矛盾的问题。提出把奇异值分解技术应用于卷积特征压缩处理，降低模型的计算与存储需求，并且采用多尺度训练方法以适应航空目标尺度的变化。实验结果表明，在通用数据集PASCAL上可以实现0.76 mAP，检测速度达16 fps，在专用航空目标数据集UCAS-AOD上可以实现0.63 mAP，检测速度达18 fps。基本满足对小目标检测精确度的要求，并且检测速度可以接近实时检测效果。

Abstract

In order to solve the problem of low recognition rate and poor positioning in aerial images

a target detection method based on deep learning is proposed. This algorithm uses VGG16 network as a fine tuning network and adds some deep network in it. Joint training is carried out by extracting the features of the shallow layers and the deep features of the target to overcome the contradiction between location and recognition in the process of detection. The singular value decomposition technology is used to compress the convolution features to reduce the computing and storage requirements of the model

and Multi scale training method is adopted to adapt to the change of aerial target scale. The experimental results show that 0.76 mAP can be implemented on the general data set PASCAL

and the detection speed is 16 fps. The 0.63 mAP can be achieved on the special aviation target data set UCAS-AOD

and the detection speed is 18 fps. It can satisfy the requirements for small target detection accuracy

and the detection speed can be close to the real-time detection effect.

关键词

Keywords

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