基于深度学习的光学遥感图像飞机检测算法

Airplane Detection of Optical Remote Sensing Images Based on Deep Learning

光学遥感图像目标检测一直都是遥感领域研究的热点之一,但现有的检测方法对背景复杂且尺寸较小的目标检测准确率不高。针对以上问题,提出了一种以Mask-RCNN为基础框架的目标检测方法。该算法以ResNet50为特征提取网络并在此基础之上利用特征重用技术来更好地提取目标的语义特征,且针对不同类型的飞机尺寸比例不固定等特点,设计了一组更加合适的候选框尺度集合。实验结果证明,该方法与以往常用的检测算法相比在小物体检测上拥有更高的检测精度。

Target detection for optical remote sensing images has always been one of the hotspots in the field of remote sensing.However,the accuracy of the existing detection methods for targets with complex background and small size is low.Aiming at the problem,a target detection method based on Mask-RCNN framework is proposed.The algorithm uses ResNet50as the feature extraction network and uses the feature reuse technology to realize better extraction of the semantic features of the target.In view of the fact that the size ratio of different types of aircrafts is not fixed,a set of more suitable candidate frame scales is designed.The experimental results show that this method has higher detection accuracy for small object detection compared with the previous detection algorithms.