改进YOLOv5的合成孔径雷达图像舰船目标检测方法

doi:10.12305/j.issn.1001-506X.2023.12.04

Abstract

Abstract:

Aiming at the problem that target detection in synthetic aperture radar (SAR) images is easily affected by noise and background interference, and the performance of ship target detection is degraded under multi-scale conditions, an improved YOLOv5 algorithm is proposed on the basis of considering the network scale and detection accuracy. In this algorithm, coordinate attention mechanism is used to suppress noise and interference to improve the feature extraction ability of the network while ensuring its lightweight advantage. The bi-directional feature pyramid is integrated to achieve multi-scale feature fusion. A new prediction box loss function is designed to improve the detection accuracy and accelerate the convergence of the algorithm. Thus, the ship target can be recognized quickly and accurately in SAR images. Experimental verification shows that the mean average presicion (mAP) of the proposed algorithm on SSDD dataset reaches 96.7%, which is 1.9% higher than that of YOLOv5s. The convergence speed is faster during training, and the network is lightweight, which has a good prospect in practical application.

Key words: synthetic aperture radar, target detection, YOLOv5, attention mechanism, multi-scale fusion

CLC Number:

TP391

Zhuzhen HE, Min LI, Yao GOU, Aitao YANG. Ship target detection method for synthetic aperture radar images based on improved YOLOv5[J]. Systems Engineering and Electronics, 2023, 45(12): 3743-3753.

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References 31

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网络架构	深度因子	宽度因子	总参数量	网络层数	FLOPs
YOLOv5n	0.33	0.25	1 765 270	270	4.5
YOLOv5s	0.33	0.5	7 022 326	270	16.5
YOLOv5m	0.67	0.75	20 871 318	369	49
YOLOv5l	1	1	46 138 294	468	109.1
YOLOv5x	1.33	1.25	86 217 814	567	205.7

软硬件	配置
操作系统	Ubuntu20.04
中央处理单元	Intel Xeon silver 4210R
图形处理单元	Nvidia TITAN RTX
显存大小/GB	24
内存大小/G	16
加速环境	Cuda 10.2
深度学习框架	Pytorch 1.12.0
Python版本	Python 3.7.13

算法	CA	BiFPN	REIOU	mAP	FPS
YOLOv5s	—	—	—	94.8	143
改进算法1	√	—	—	95.0	139
改进算法2	—	√	—	95.5	127
改进算法3	—	—	√	95.3	156
改进算法4	√	√	—	96.3	128
改进算法5	—	√	√	95.9	128
改进算法6	√	—	√	96.1	139
本文算法	√	√	√	96.7	127

检测算法	mAP	FPS	FLOPs/G	Para/M	Weights/M
Faster-RCNN ResNet+FPN	83.7	16.7	134.5	41.4	330.3
RetinaNet	92.1	23.4	127	53.1	257.1
SSD	86.0	41.0	15.0	13.1	105.1
YOLOv3	96.1	36.9	155.3	61.5	123.6
YOLOv5s	94.8	143	16.5	7.0	13.7
本文算法	96.7	127	16.6	7.2	14.1

嵌入方案	mAP	FPS	FLOPs/G	Para/M
方案1	96.4	195	14.8	6.6
方案2(本文方案)	96.7	127	16.6	7.2

Ship target detection method for synthetic aperture radar images based on improved YOLOv5

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检测算法	SE	CBAM	CA	CIOU	SIOU	EIOU	REIOU	BiFPN	Precision	Recall	mAP	FPS	Para
YOLOv5s	—	—	—	√	—	—	—	—	93.5	88.8	94.8	143	7 022 326
改进算法1	√	—	—	—	√	—	—	√	93.1	89.0	95.0	132	7 203 711
改进算法2	—	√	—	—	√	—	—	√	93.3	88.9	95.1	130	7 203 809
改进算法3	—	—	√	—	√	—	—	√	94.1	88.6	96.0	130	7 196 591
改进算法4	√	—	—	—	—	√	—	√	92.5	90.8	95.1	133	7 203 711
改进算法5	—	√	—	—	—	√	—	√	92.4	91.0	95.2	126	7 203 809
改进算法6	—	—	√	—	—	√	—	√	93.0	91.3	96.5	127	7 196 591
改进算法7	√		—	—	—	—	√	√	92.5	90.9	95.3	130	7 203 711
改进算法8	—	√	—	—	—	—	√	√	92.9	89.4	95.1	127	7 203 809
本文算法	—	—	√	—	—	—	√	√	93.7	90.2	96.7	127	7 196 591

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