Detection network of critical parts for remote sensing ship based on semantic features

卷积层	通道数（输入）	通道数（输出）	大小（输入）	大小（输出）
Deconv 1	4 096	2 048	W×H	2W×2H
Conv 1	2 048	1 024	2W×2H	2W×2H
Deconv 2	2 048	1 024	2W×2H	4W×4H
Conv 2	1 024	512	4W×4H	4W×4H
Conv 3	512	128	4W×4H	4W×4H
Conv 4	128	64	4W×4H	4W×4H
Conv 5	64	2	4W×4H	4W×4H

Table 2. Details of CP-Ship dataset
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Table 2. Details of CP-Ship dataset
数据集 CP-Ship
图像数量关重部位个数
训练集 812 2 295
测试集 203 561
总计 1 015 2 856

Table 3. Size selection of expansion structure element
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Table 3. Size selection of expansion structure element
结构元素尺寸 AP/% 结构元素尺寸 AP/%
3×3 68.67 13×13 71.38
5×5 69.69 15×15 70.97
7×7 70.28 17×17 69.94
9×9 71.25 19×19 68.32
11×11 71.76 21×21 66.46

Table 4. Results of ablation experiment
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Table 4. Results of ablation experiment
RetinaNet 角度参数 FE-FPN SMN AP/%
√ 60.41
√ √ 62.64
√ √ √ 66.45
√ √ √ √ 71.76

Table 5. Quantitative results of different network models

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Table 5. Quantitative results of different network models

类别	方法	主干网络	锚框类型	TP	FP	FN	AP/%	FPS
双阶段网络	Faster R-CNN^[9]	ResNet-50	水平框	411	152	150	65.78	19.2
	ReDet^[25]	ResNet-50	旋转框	454	103	107	75.24	3.1
	Dynamic R-CNN^[26]	ResNet-50	水平框	400	130	161	63.85	20.5
	Oriented R-CNN^[27]	ResNet-50	旋转框	423	169	138	68.39	11.8
单阶段网络	YOLOv3^[28]	DarkNet-53	水平框	413	84	148	66.49	51.8
	R3Det^[15]	ResNet-50	旋转框	393	146	168	64.28	11.3
	RetinaNet^[13]	ResNet-50	水平框	387	229	174	60.41	23.2
	CFA^[29]	ResNet-50	旋转框	414	225	306	63.61	19.3
	CPDNet	ResNet-50	旋转框	433	150	128	71.76	12.5

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Dongdong ZHANG, Chunping WANG, Qiang FU. Detection network of critical parts for remote sensing ship based on semantic features[J]. Journal of Applied Optics, 2023, 44(3): 595

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