基于卷积神经网络与注意力机制的SAR图像飞机检测

doi:10.12305/j.issn.1001-506X.2021.11.20

Abstract

Abstract:

In the application field of synthetic aperture radar (SAR) images, the detection of aircraft target in SAR images has attracted much attention. Aiming at the problems of high computational complexity and low detection performance of existing detection algorithm models, an aircraft detection in SAR images algorithm based on depthwise separable convolutional neural network and attention mechanism is proposed. Firstly, the depthwise separable convolutional neural network is used to extract image features, and the inverted residual block is introduced into the network to effectively reduce the loss of feature information caused by channel slimming. Secondly, the multi-scale dilated convolution spatial attention mechanism module and global context channel attention mechanism module are introduced into the network. By redistributing more representative weights to salient regions and each feature map, the spatial effective information and semantic correlation between channels can be better captured, and the ability of feature expression can be enhanced. Finally, the comparative experimental verification is carried out on the SAR aircraft dataset (SAD). Experimental results show that this algorithm has a better detection effect, the average precision is 86.3%, and the detection speed is 22.4 fps/s.

Key words: synthetic aperture radar (SAR) images, aircraft detection, depthwise separable convolution, attention mechanism

CLC Number:

TP751

Guangshuai LI, Juan SU, Yihong LI, Xiang LI. Aircraft detection in SAR images based on convolutional neural network and attention mechanism[J]. Systems Engineering and Electronics, 2021, 43(11): 3202-3210.

Figures/Tables 15

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References 31

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方法	C₃∶113×75	C₄∶57×38	C₅∶29×19
原始Faster R-CNN
融合注意力机制

方法	AP/%	R/%	P/%	F-score/%	mIoU/%	FPS
Faster R-CNN-1	79.8	79.9	73.1	76.3	71.3	16.8
Faster R-CNN-2	81.7	82.9	75.8	79.2	71.9	16.2
Faster R-CNN-3	82.8	83.8	76.7	80.1	72.5	13.7
本文方法	86.3	88.6	81.0	84.6	74.4	22.4

方法	参数量/×10⁶	模型大小/MB	训练时长/h
Faster R-CNN-1	—	1 092.5	—
Faster R-CNN-2	61.31	409.8	5.74
Faster R-CNN-3	80.30	561.9	7.07
本文方法	38.55	225.0	4.45

序号	算法				AP/%	FPS
序号	DS-Conv	MDC-SA	GC	W	AP/%	FPS
1	—	—	—	—	81.7	16.2
2	√	—	—	—	80.2	24.3
3	√	—	—	√	81.9	24.3
4	√	√	—	—	83.8	23.1
5	√	—	√	—	83.1	23.8
6	√	√	√	—	85.2	22.4
7	√	√	√	√	86.3	22.4

方法	密集分布	独立分布	大幅面场景	复杂场景
标注框
Faster R-CNN-1
Faster R-CNN-2
Faster R-CNN-3
本文方法

Aircraft detection in SAR images based on convolutional neural network and attention mechanism

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