基于感知向量的光学遥感图像舰船检测

doi:10.12305/j.issn.1001-506X.2022.12.06

Abstract

Abstract:

To solve the problem of severe interference and high false positive rate in ship detection from remote sensing images, an enhanced method based on the box boundary-aware vectors (BBAVectors) detection network is proposed.Firstly, a supervised attention module is added to the feature fusion network to enhance the relevant information within the target region and reduce the interference of irrelevant background information. Then a self-supervised loss function is proposed based on the geometric relations among the boundary vectors to guarantee the coupling relation between vectors and prevent the irregular shape of the bounding boxes caused by vectors' independence. Experimental results in the L₂ level detection task on the HRSC2016 dataset show that the mean average precision of the detection results for the proposed model gets improved by 6.91% compared with the original network. The proposed model can effectively suppress the interference of background noise and reduce the false alarm rates in near-shore ship detection, which demonstrates its effectiveness.

Key words: optical remote sensing images, ship targets detection, box boundary-aware vectors (BBAVectors), supervised, attention module

CLC Number:

P237

Chaofan PAN, Runsheng LI, Yan XU, Qing HU, Chaoyang NIU, Wei LIU. Ship detection of optical remote sensing images based on aware vectors[J]. Systems Engineering and Electronics, 2022, 44(12): 3631-3640.

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References 29

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方法	mAP(07)	mAP(12)	推理速度/fps
CP^[24]	55.70	-	-
BL2^[24]	69.60	-	-
R²CNN^[10]	73.07	79.73	5.00
IENet^[25]	75.01	-	-
RC1^[24]	75.70	-	-
RC2^[24]	75.70	-	-
RRPN^[11]	79.08	85.64	1.50
R²PN^[26]	79.60	-	-
RRD^[27]	84.30	-	-
RoI Trans.^[28]	86.20	-	5.90
DRN^[29]	-	92.70	-
基线网络	88.83	90.75	13.40
本文方法	90.06	94.50	12.68

目标	方法
目标	基线网络	本文方法
航母	80.90	97.65
战舰	92.54	94.1
商船	64.66	68.84
潜艇	67.19	77.62
mAP(07)	74.2	83.05
mAP(12)	76.33	84.55

目标	方法
目标	基线网络	基线网络+注意力模块
航母	80.90	89.32
战舰	92.54	92.63
商船	64.66	63.19
潜艇	67.19	76.24
mAP(12)	76.33	80.35

目标	方法
目标	基线网络	基线网络+L_vec
航母	80.90	93.85
战舰	92.54	92.86
商船	64.66	64.84
潜艇	67.19	76.81
mAP(12)	76.33	82.09

weight₁	weight₂	航母	战舰	商船	潜艇	mAP(12)
1	0.3	89.99	93.43	69.15	75.28	81.96
1	0.5	93.83	93.74	71.53	79.28	84.59
1	0.8	91.19	94.81	70.49	79.02	83.88
1	1	97.65	94.10	68.84	77.62	84.55
1	2	95.64	94.82	71.17	71.00	83.15
1	3	87.17	93.29	67.89	75.54	80.97
1	4	93.27	94.56	66.78	69.27	80.97
2	1	88.07	92.99	66.52	70.02	79.40
3	1	93.62	93.16	68.07	65.09	79.99
4	1	93.27	94.25	70.25	65.61	80.84

Ship detection of optical remote sensing images based on aware vectors

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λ₁	λ₀	航母	战舰	商船	潜艇	mAP(12)
1	1	91.08	93.22	67.81	74.28	81.60
2	1	89.12	95.04	68.51	81.94	83.65
3	1	97.65	94.10	68.84	77.62	84.55
4	1	94.47	93.71	70.83	74.60	83.40
5	1	94.87	94.21	72.04	69.40	82.63

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