Précédent|Prochain

Joint dual attention mechanism and bidirectional feature pyramid for remote sensing small targets detection

  • role： First author第一作者
  • Affiliation：

    National Energy Group Jianbi Power Plant, Zhenjiang 212006, China

  • Email：12030141@ceic.com
  • Introduction：李科文，研究方向为基于无人机遥感的新能源科技创新。E-mail： 12030141@ceic.com
LI Kewen1,  
  • Affiliation：

    Yellow River Engineering Consulting Co., Ltd., Zhengzhou 450003, China

ZHU Guanglei2,  
  • Affiliation：

    National Energy Group Jianbi Power Plant, Zhenjiang 212006, China

WANG Hui1,  
  • Affiliation：

    National Energy Group Jianbi Power Plant, Zhenjiang 212006, China

ZHU Rui1,  
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

    Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing 211100, China

DI Xiyao34,  
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

    Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing 211100, China

ZHANG Tianjian34,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

    Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing 211100, China

  • Email：zhaohui.xue@hhu.edu.cn
  • Introduction：薛朝辉，研究方向为深度学习等先进机器学习模型在高光谱遥感影像小样本分类、多源遥感数据迁移学习、地表参数遥感估算等方面的理论与应用研究。E-mail： zhaohui.xue@hhu.edu.cn
XUE Zhaohui34*

résumé

Given imaging characteristics and limitations in spatial resolution, feature extraction for small targets is difficult, which increases the hardship of small-target detection. Existing deep learning target detection network architectures are mostly based on natural images and insufficient for the research on and exploration of small targets in remote sensing images. To overcome these issues, this study proposes a remote sensing image small-target detection algorithm that combines a dual attention mechanism and a bidirectional feature pyramid.This work provides innovative contributions. (1) To solve the problems of small-target occupation in remote sensing images and the usually huge parameter size, we introduce the LKG bottleneck and generalized intersection over union loss function into YOLOv3 and propose the LKGNet-YOLO network. (2) To resolve the noise disturbance issue and the drawback of feature fusion, we introduce the DA-LKGNet bottleneck and bidirectional feature pyramid network into LKGNet-YOLO and propose the DA-LKGNet-YOLO network.The remote sensing image dataset (UA) released by the University of Chinese Academy of Sciences in 2014 and the AI-TOD dataset released by Wuhan University in 2021 is used for experiments to validate the effectiveness of the proposed method in remote sensing image small-target detection. Experimental results demonstrate that the proposed method achieves a mean average precision (mAP) of 96.21% at a threshold of 0.5 on the UA dataset. On the AI-TOD dataset, the mAP0.5—0.95 is 9.51% at thresholds ranging from 0.5 to 0.95. Compared with YOLOv3, RFBNet, SSD, FSSD, RetinaNet, and RefineDet, the proposed method has an mAP accuracy that is 3.45%—7.52% and 1.36%—4.84% higher, indicating a considerable performance improvement. Meanwhile, its detection level on small-scale targets is better than that of Faster-RCNN and YOLOv7 algorithms. Compared with the original YOLOv3, our method reduces the number of floating-point operations per second by 48% and the number of parameters by 42%.A small-target detection method for remote sensing images is proposed in this study. Its contribution lies in the utilization of the YOLOv3 model with LKGNet as the backbone network, along with the design of a dual attention mechanism and a bidirectional feature pyramid network, resulting in a lightweight DA-LKGNet-YOLO model. This model can effectively extract small-sized objects in complex remote sensing images. The experimental results confirm the effectiveness of the proposed method.

mots-clés

remote sensing image;Small Target Detection;deep learning;YOLOv3;Attention Mechanism;feature pyramid

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