光学学报, 2019, 39 (2): 0210001, 网络出版: 2019-05-10
基于特征融合与软判决的遥感图像飞机检测 下载: 1004次
Airplane Detection Based on Feature Fusion and Soft Decision in Remote Sensing Images
图像处理 飞机检测 特征融合 软判决 区域卷积神经网络 image processing airplane detection feature fusion soft decision region-based convolutional neural network
摘要
提出了一种特征融合结合软判决的飞机检测方法。以区域卷积神经网络为基本框架,依次采用L2范数归一化、特征连接、尺度缩放和特征降维来融合多层特征。为了降低网络在目标高度重叠时的漏检率,引入软判决来改进传统的非极大值抑制方法。实验结果表明,所提方法能够准确快速地检测到飞机,得到检测率为94.25%、虚警率为5.5%、平均运行时间为0.16 s的实验结果。与现有的其他检测方法相比,所提方法的各项指标均得到显著提升。
Abstract
An airplane detection method is proposed based on feature fusion and soft decision, in which the region-based convolutional neural network is used as the basic framework and the L2 normalization, feature connection, scaling, and dimensionality reduction are in turn used to fuse the multi-layer features. The soft decision, which can improve the traditional non-maximum suppression method, is introduced in order to reduce the detection-omission-rate of grids in the case of significant overlap of targets. The experimental results show that the proposed method can be used to detect airplanes accurately and quickly with a detection rate of 94.25%, a false alarm rate of 5.5%, and the average running time of 0.16 s. Compared with those of the other existing detection methods, each index of the proposed method is significantly improved.
朱明明, 许悦雷, 马时平, 李帅, 马红强. 基于特征融合与软判决的遥感图像飞机检测[J]. 光学学报, 2019, 39(2): 0210001. Mingming Zhu, Yuelei Xu, Shiping Ma, Shuai Li, Hongqiang Ma. Airplane Detection Based on Feature Fusion and Soft Decision in Remote Sensing Images[J]. Acta Optica Sinica, 2019, 39(2): 0210001.