改进的SSD算法及其对遥感影像小目标检测性能的分析

Improved SSD Algorithm and Its Performance Analysis of Small Target Detection in Remote Sensing Images

针对以Faster R-CNN为代表的基于候选框方式的遥感影像目标检测方法检测速度慢,而现有SSD算法在小目标检测中性能低的问题,提出一种改进的SSD算法,综合利用现有基于候选框方式和一体化检测方式的优势,提升检测性能。该算法利用密集连接网络替换原有的VGGNet作为骨干网络,并且在密集连接模块之间构建特征金字塔,代替原有多尺度特征图。为验证所提算法的精度及性能,设计样本数据在线采集系统,并采集飞机及运动场目标样本集作为实验样本,通过对改进SSD算法的训练,验证了其网络结构的稳定性,在无迁移学习支持下依然能够达到良好效果,且训练过程不易发散。通过对比以101层的残差网络(ResNet101)作为基础网络的Faster R-CNN算法和R-FCN算法可知,改进SSD算法较Faster R-CNN算法和R-FCN算法的MAP在测试集上分别提升了9.13%和8.48%,小目标检测的MAP分别提升了14.46%和13.92%,检测单张影像耗时71.8 ms,较Faster R-CNN和R-FCN算法分别减少45.7 ms和7.5 ms。

An improved single shot multibox detector(SSD) algorithm is proposed aiming at the problems of slow detection speed of the target proposal based remote sensing image target detection method represented by faster regions with convolutional neural network(R-CNN) and the low performance in small target detection by the SSD algorithm. The algorithm can combine the advantages of the existing detection methods based on target proposal and one-stage target detection to improve the target detection performance. Furthermore, the algorithm replaces the original visual geometry group net with a densely connected network as the backbone network and constructs a feature pyramid between the densely connected modules instead of the original multi-scale feature map. A sample data online acquisition system is designed to verify the accuracy and performance of the proposed algorithm. A sample set of aircraft and playground target is collected as the experimental sample. The network structure stability is verified by training the improved SSD algorithm. Consequently, good results can be achieved without the support of transfer learning. Moreover, the training process is not easy to diverge. By comparing the Faster R-CNN algorithm using ResNet101 as the backbone network and the R-FCN(region-based fully convolutional networks) algorithm, we find that the mean average precision(MAP) of the improved SSD algorithm is 9.13% and 8.48% higher than that of the faster R-CNN and R-FCN algorithms in the test set, respectively. The proposed SSD algorithm improves the MAP in the small target detection by 14.46% and 13.92% compared to the faster R-CNN and R-FCN algorithms, respectively. Detecting a single image takes 71.8 ms, which is 45.7 ms and 7.5 ms less than that of the faster R-CNN and R-FCN algorithms, respectively.