深度卷积神经网络支持下的遥感影像飞机检测

doi:10.13474/j.cnki.11-2246.2019.0177

测绘通报 ›› 2019, Vol. 0 ›› Issue (6): 19-23.doi: 10.13474/j.cnki.11-2246.2019.0177

深度卷积神经网络支持下的遥感影像飞机检测

谢梦¹, 刘伟^1,2, 杨梦圆¹, 柴琪¹, 吉莉¹

1. 江苏师范大学地理测绘与城乡规划院, 江苏徐州 221116;
2. 资源与环境信息系统国家重点实验室, 北京 100101

收稿日期:2018-12-14 修回日期:2019-02-12 出版日期:2019-06-25 发布日期:2019-07-01
通讯作者: 刘伟。E-mail:grid_gis@126.com E-mail:grid_gis@126.com
作者简介:谢梦(1994-),女,硕士生,研究方向为遥感图像处理与目标检测,以及图像特征识别。E-mail:15262036926@163.com
基金资助:
国家自然科学基金（41601405）；江苏省国土资源科技计划项目（2018054）；资源与环境信息系统国家重点实验室开放基金；江苏师范大学2018年研究生科研创新计划项目（2018YXJ040）

Remote sensing image aircraft detection supported by deep convolutional neural network

XIE Meng¹, LIU Wei^1,2, YANG Mengyuan¹, CHAI Qi¹, JI Li¹

1. School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China;
2. State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China

Received:2018-12-14 Revised:2019-02-12 Online:2019-06-25 Published:2019-07-01

摘要/Abstract

摘要：

针对YOLOv3算法对小目标检测较差及出现较多漏检的问题，本文提出了一种优化的YOLOv3算法。首先使用K-means算法计算出与数据集相适用的锚框；其次将扩张卷积引入到YOLOv3网络，用来增强网络高层的感受野，改善小目标的检测效果；然后使用深度可分离卷积取代YOLOv3网络残差模块中的普通卷积，可减少计算量，从而得到一种新型卷积神经网络结构；最后在数据集上进行对比试验。结果表明，优化的YOLOv3算法能够检测出更多目标，降低漏检率，相比于YOLOv3算法，其召回率提高11.86%，F1-score提高2.99%。

关键词: YOLOv3, 遥感影像, 目标检测, 扩张卷积, 深度可分离卷积

Abstract: Aiming at the problem that YOLOv3 (You look only once) algorithm has poor detection of small targets and many missed detection, this paper proposes an optimized YOLOv3 algorithm. Firstly, K-means algorithm is used to calculate the anchor frame suitable for the data set in this paper. Then, extended convolution is introduced into YOLOv3 network to enhance the high-level perception field of the network, improve the detection effect of small targets, and secondly, depthwise separable convolution is used. It replaces the ordinary convolution in YOLOv3 network residual module, reduces the calculation parameters, and thus obtains a new convolution neural network structure. Then the comparative experiments are carried out on the data set in this paper. The experimental results show that the optimized YOLOv3 algorithm can detect more targets and reduce the missed detection rate. Compared with YOLOv3 algorithm, its recall rate is increased by 11.86% and F1-score by 2.99%.

Key words: YOLOv3, remote sensing image, target detection, dilated convolution, depthwise separable convolutions

中图分类号:

P237

谢梦, 刘伟, 杨梦圆, 柴琪, 吉莉. 深度卷积神经网络支持下的遥感影像飞机检测[J]. 测绘通报, 2019, 0(6): 19-23.

XIE Meng, LIU Wei, YANG Mengyuan, CHAI Qi, JI Li. Remote sensing image aircraft detection supported by deep convolutional neural network[J]. 测绘通报, 2019, 0(6): 19-23.

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深度卷积神经网络支持下的遥感影像飞机检测

Remote sensing image aircraft detection supported by deep convolutional neural network

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