改进Faster R-CNN的光学遥感飞机目标检测

doi:10.11896/jsjkx.210300121

摘要/Abstract

摘要： 光学遥感图像飞机目标检测技术已广泛应用于航空交通和军事侦察等领域。针对检测多种不同尺寸飞机时小目标漏检率和虚警率高以及目标定位精度低导致的检测准确率不高的问题,文中在Faster R-CNN算法的基础上提出了一种基于双线性插值的改进ROI pooling方案,用于飞机目标检测,解决了两次量化导致的区域失配问题。实验结果表明,改进方法取得了AP(IOU≤0.5)为95.35%的检测性能,在针对多尺寸飞机检测的任务中,提高了目标定位精度和平均准确率。

关键词: Faster R-CNN, ROI pooling, 飞机目标检测, 光学遥感图像, 深度学习

Abstract: Optical remote sensing image airplane target detection technology has been widely used in the fields of air traffic and military reconnaissance.Aiming at the problem of low detection accuracy and high false alarm rate when detecting multiple aircraft of different sizes,this paper proposes an improved ROI pooling method based on bilinear interpolation for aircraft target detection,which solves the problem of mis-alignment.Experimental results show that the improved method achieves a detection performance of 95.35% AP(IOU≤0.5).In the task of multi-size airplane detection,the target positioning accuracy and average accuracy are improved.

Key words: Airplane target detection, Deep learning, Faster R-CNN, Remote sensing image, ROI pooling

中图分类号:

TP753

祝文韬, 兰先超, 罗唤霖, 岳彬, 汪洋. 改进Faster R-CNN的光学遥感飞机目标检测[J]. 计算机科学, 2022, 49(6A): 378-383. https://doi.org/10.11896/jsjkx.210300121

ZHU Wen-tao, LAN Xian-chao, LUO Huan-lin, YUE Bing, WANG Yang. Remote Sensing Aircraft Target Detection Based on Improved Faster R-CNN[J]. Computer Science, 2022, 49(6A): 378-383. https://doi.org/10.11896/jsjkx.210300121

参考文献

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