基于关键点的遥感图像舰船目标检测

doi:10.12305/j.issn.1001-506X.2022.08.07

Abstract

Abstract:

Problems with current ship target detection method, such as high computational cost of anchor frame traversal; and the rotation invariance of the features extracted from the backbone network is weak and cannot adapt to the ship targets in any direction, resulting in inconsistency. Therefore, a ship target detection method based on key points in remote sensing images is proposed, and the target detection is realized by predicting the ship center point. First, the depth separable convolution is added to reduce the parameter redundancy, and the attention to the ship target is enhanced combined with SimAM nonparametric attention. Second, the orientation-invariant model (OIM) is introduced to generate the orientation-invariant feature map to enhance the adaptability of the network to target rotation. Finally, considering that the ship targets in remote sensing images are densely arranged in any direction, but the center point of the ship target is constant, the idea of directly predicting the center point of the target, and then regressing the offset, target scale and angle is adopted to get rid of the anchor frame traversal mechanism and improve the detection speed. A comparative experiment was conducted on the HRSC2016 and RFUE2021 datasets, and the experimental results fully demonstrate the effectiveness of the proposed method.

Key words: arbitrary-oriented ship detection, center point estimation, SimAM attention, orientation-invariant model (OIM)

CLC Number:

Tao ZHANG, Xiaogang YANG, Ruitao LU, Xueli XIE, Chuang LIU. Key-point based method for ship detection in remote sensing images[J]. Systems Engineering and Electronics, 2022, 44(8): 2437-2447.

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实验参数	参数配置
优化器	Adam
初始学习率	5×4e
Epoch	230
初始降采样率	R=4

算法	输入大小	mAP_0.5	mAP_0.7	F1	FPS
Res18_dcn	512×512	62.34	58.70	65.12	34.2
DLASeg	512×512	71.52	65.34	74.98	44.3
Hourglass	512×512	73.18	68.46	78.67	13.6
Ours	512×512	77.96	72.09	81.56	41.2
Ours	1 024×1 024	80.57	75.42	83.37	15.4

方法	主干网络	输入大小/像素	mAP_0.5	mAP_0.7	FPS
R²CNN^[24]	Resnet50	512×512	75.09	63.83	10.3
RetinaNet-Rbb^[25]	Resnet50	512×512	70.49	62.82	35.6
SCRDet^[26]	Resnet50	512×512	72.90	63.04	9.2
CSL^[27]	Resnet50	512×512	70.73	61.25	10.4
R³Det^[28]	Resnet50	512×512	67.47	60.17	14.0
RSDet^[29]	Resnet50	512×512	70.74	61.52	15.4
S²A-Net^[30]	Resnet50	512×512	77.19	72.65	33.1
CenterNet-Rbb	DLA34	512×512	71.82	67.52	41.09
CenterNet-Rbb	Hourglass	512×512	72.13	69.21	13.7
Ours¹⁾	DLASeg	512×512	77.96	72.09	41.23

方法	主干网络	输入尺寸/像素	mAP
R²CNN^[24]	Resnet101	800×800	73.1
PPRN^[31]	Resnet101	800×800	79.1
R²PN^[13]	VGG16	800×800	79.6
ROI-Trans^[12]	Resnet101	512×800	86.2
RSDet^[29]	Resnet101	800×800	86.5
CenterNet-Rbb	Hourglass	1 024×1 024	80.1
Ours¹⁾	DLASeg	1 024×1 024	86.7

Baseline	DSC	SinAM	OIM	mAP_0.5	FPS
√				71.82	41.09
√	√			70.09	43.56
√	√	√		73.38	42.89
√	√	√	√	77.96	41.23

Key-point based method for ship detection in remote sensing images

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References 31

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Self-attention	Backbone	mAP/%
+SE	DLASeg	76.54
+CBAM	DLASeg	77.28
+SimAM	DLASeg	77.96

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