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Remote Sensing for Natural Resources    2021, Vol. 33 Issue (3) : 97-106     DOI: 10.6046/zrzyyg.2020372
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Ship detection based on multi-scale feature enhancement of remote sensing images
LIU Wanjun(), GAO Jiankang(), QU Haicheng, JIANG Wentao
College of Software, Liaoning Technical University, Huludao 125105, China
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Abstract  

Aiming at the omission in the ship target detection from remote sensing images with complex background caused by the arbitrary and dense arrangement of ships, this study, based on the rotation region generation network, proposes a ship target detection algorithm using the multi-scale feature enhancement of remote sensing images. The detailed steps are as follows. Firstly, improve the feature pyramid network using the receptive field module with dense connection at the feature extraction stage. Then obtain the characteristics of multi-scale receptive fields using the convolution of different dilate rates. In this way, the expression of high-level semantic information can be enhanced. Then design a feature fusion structure based on attention mechanisms to restrain noise and highlight the target characteristics. Afterward, fuse all layers according to the spatial weight value of each layer to obtain a feature layer that takes both semantic and position information into account. Then conduct attention enhancement to the features of this layer, and integrate the enhanced features into the original feature layer in the pyramid network. Consequently, pay more attention to target locations by increasing attention loss and optimizing the attention network according to the classification and regression loss. As indicated by the experiment results of DOTA remote sensing dataset, the average precision of this algorithm is as high as 71.61%, which is higher than the latest ship target detection algorithm based on remote sensing images. In this manner, the omission in ship target detection can be effectively solved.
Keywords convolution neural network      multi-scale feature fusion      attention mechanism      remote sensing image      ship target detection     
ZTFLH:  TP751.1  
Corresponding Authors: GAO Jiankang     E-mail: liuwanjun@lntu.edu.cn;1554797460@qq.com
Issue Date: 24 September 2021
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Wanjun LIU
Jiankang GAO
Haicheng QU
Wentao JIANG
Cite this article:   
Wanjun LIU,Jiankang GAO,Haicheng QU, et al. Ship detection based on multi-scale feature enhancement of remote sensing images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 97-106.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020372     OR     https://www.gtzyyg.com/EN/Y2021/V33/I3/97
Fig.1  Feature pyramid network structure
Fig.2  The representation of oriented bounding box
Fig.3  Overall framework
Fig.4  The module of DCRF
Fig.5  Feature fusion structure
Fig.6  Dual attention network
Fig.7  Visualization of the attention network
实验方法 召回率/% 精确率/% AP/% 检测时间/s
基础网络 76.56 87.86 67.37 0.21
+DCRF 80.82 85.92 69.52 0.21
+AFF 80.99 85.82 69.66 0.22
本文方法 81.74 87.04 71.61 0.22
Tab.1  Results of ablative experiments of different module
类型 示例1 示例2 示例3
基础网络结果图
DCRF结果图
MFEDet结果图
基础网络特征图
MFEDet特征图
Tab.2  Show the results of different modules
对比方法 召回率 精确率 AP
FR-O 58.53 65.60 39.24
R-DFPN 67.85 87.67 59.78
RRPN 69.35 89.68 63.42
RADet — — 68.86
本文方法 81.74 87.04 71.61
Tab.3  Different methods comparison results(%)
方法 训练时间 测试时间
FR-O 0.34 0.10
RRPN 0.85 0.35
R-DFPN 1.15 0.38
Baseline 0.58 0.21
本文方法 0.64 0.22
Tab.4  Training time and test time for each method(s)
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