基于中心点和语义信息的多方向遥感舰船检测

doi:10.12305/j.issn.1001-506X.2022.06.02

Abstract

Abstract:

Ship detection is a hotspot in the field of high-resolution remote sensing image interpretation. Aiming at the challenges of dense arrangement, different directions, and complex background of ships in remote sensing images, a rotated CenterNet using semantec information (RSI-CenterNet) multidirectional ship target detection method is proposed. First, we add a branch to predict the angle of objects in CenterNet. Second, a semantic segmentation branch is added, and the feature of semantic segmentation branch is used to guide the detection head to locate the center point more accurately. Finally, an attention module is introduced to enhance the feature of the significant areas and channels. Experimental results show that compared with other advanced methods, the proposed method has higher detection accuracy and detection speed, with an average accuracy of 88.31% and detection speed of 17.8 FPS in HRSC2016 dataset.

Key words: high-resolution remote sensing images, ship target, key-point detection, semantic information, multi-direction detection

CLC Number:

TN957

Runlin LI, Huanxin ZOU, Xu CAO, Fei CHENG, Shitian HE, Meilin LI. Multi-direction remote sensing ship detection based on center point and semantic information[J]. Systems Engineering and Electronics, 2022, 44(6): 1772-1781.

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References 32

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性能参数	舰船目标检测方法
性能参数	R2CNN	RR-CNN	RRPN	RNet-H	RRD	RoI-T	RSI-C
骨干网络	R101	VGG16	R101	R101	VGG16	R101	R101
输入尺寸	800×800	-	800×800	800×800	384×384	512×800	512×512
检测速度/FPS	5	-	1.5	14	-	6	17.8
AP/%	73.07	75.7	79.08	82.89	84.3	86.2	88.31

网络模型	CBAM	语义信息	AP/%	检测速度/FPS	参数量/M
R-C	-	-	80.05	24.63	121.0
R-C+改进	√	-	85.27	22.73	121.1
	-	√	87.43	22.00	129.1
	√	√	87.81	21.16	129.2

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Multi-direction remote sensing ship detection based on center point and semantic information

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