[1]张永福,宋海林,班 越,等.融合特征的深度学习遥感图像目标检测模型[J].计算机技术与发展,2021,31(09):48-54.[doi:10. 3969 / j. issn. 1673-629X. 2021. 09. 009]
　ZHANG Yong-fu,SONG Hai-lin,BAN Yue,et al.Fusion Features Based Deep Learning Remote Sensing Image Target Detection Model[J].,2021,31(09):48-54.[doi:10. 3969 / j. issn. 1673-629X. 2021. 09. 009]
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融合特征的深度学习遥感图像目标检测模型()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年09期
页码:
48-54
栏目:
图形与图像
出版日期:
2021-09-10

文章信息/Info

Title:
Fusion Features Based Deep Learning Remote Sensing Image Target Detection Model
文章编号:
1673-629X(2021)09-0048-07
作者:
张永福; 宋海林; 班 越; 汪西莉
陕西师范大学 计算机科学学院,陕西 西安 710119
Author(s):
ZHANG Yong-fu; SONG Hai-lin; BAN Yue; WANG Xi-li
School of Computer Science,Shaanxi Normal University,Xi’an 710119,China
关键词:
遥感图像; 目标检测; 卷积神经网络; 特征融合; 后处理
Keywords:
remote sensing image; object detection; convolutional neural network; feature fusion; post processing
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 09. 009
摘要:
为了提高目标检测模型对遥感图像中排列密集、尺度不一的目标,特别是小目标的检测性能,提出了融合特征的深度学习遥感图像目标检测模型和方法。模型采用小规模的网络结构,以应对标记样本较少的情况,并提出了融合多级特征的策略获取更为有效的特征,使模型在不增加检测时间的同时,提高遥感图像中较为密集且大小不一的目标的检测精度。 模型中提出了一种新的后处理算法——分组融合剔除检测框算法,在剔除冗余检测框的同时微调检测框位置,使检测框对目标定位更精确,进一步提升检测精度。实验结果表明,所提模型在 UCAS-AOD 和 RSOD-? Dataset 数据集上检测飞机,精度比 Faster R-CNN 的结果提高了 4.2% 和 7.3% ,漏检率和误检率均有降低。 在 UCAS-AOD 数据集上检测更小的汽车目标, 所提模型比 Faster R-CNN 检测精度提高了 7. 9% , 漏检率下降了 5.91% ,误检率下降了 2.06% 。 和 FasterR-CNN 相比,所提的融合处理和检测框后处理算法使得模型针对复杂场景中多尺度密集目标和小目标取得了更高的检测性能。
Abstract:
In order to improve the performance of the object detection model for dense,different sizes targets,especially for small targets,in remote sensing images,a fusion features based deep learning remote sensing image target detection model is proposed. The model adopts small-scale network structure aiming at the small labeled sample data sets. The strategy of multi-level feature fusion is proposed to obtain more effective features,so that the model can improve the detection accuracy for dense and different sizes targets in remote sensing images without increasing the detection time. A new post-processing algorithm,namely packet fusion reject detection bounding boxes algorithm,is proposed,which can remove the redundant detection bounding box and fine-tune the position of the detection box,thus improving the box’s locating accuracy and further improving the detection accuracy. The experimental results on the UCAS-AODand RSOD-Dataset remote sensing datasets for detecting aircraft and vehicle targets show that the proposed model achieves high detection performance for multi-scale and dense targets on small sample datasets. Compared with Faster R- CNN, it improves the detection accuracy by 4.2% and 7.3% for plane target,and by 7.9% for car targets,and reduces the missing ratio by 5. 91% and false alarm rate by 2.06% . Compared with the existing models for dense and different sizes targets detection in complex remote sensing image, the proposed model achieves higher performance due to the feature fusion processing and the detection post-processing algorithm.

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更新日期/Last Update: 2021-09-10