基于深度学习的舰船关键部位检测算法

doi:10.12305/j.issn.1001-506X.2026.02.04

Abstract

Abstract:

Aiming at the problems that critical parts detection algorithm and corresponding data set are lacking, accuracy and speed of detection algorithm can not be balanced, and the network is not robust to ship position scaling, a multi-scale feature fusion and three dimensional feature enhancement method of ship critical parts detection without anchor frame: similarity-based attention module-deep layer aggregation segmentation algorithm is proposed to achieve precise and efficient detection of critical parts of ships. This method can achieve accurate and efficient detection of critical parts of ships. Firstly, the receptive fields block is introduced to realize multi-scale feature fusion and improve the detection accuracy. Then, by incorporating similarity-based attention module, attention to useful target information is improved in the network. by using deformable convolution to aggregate the feature information of different layers, the generalization ability and expression ability of the network are effectively improved. Finally, on the premise of not using anchor frame and improving the detection speed, the network get the center point offset, target angle, scale information through the target center point prediction, and then regression. Comparative experiments are carried out on the self-built data set and PASCAL VOC data set respectively, which fully proved the accuracy and timeliness of the proposed network detection of ship critical parts, and at the proposed network could provide feasible technical approaches and theoretical support for anti-ship equipment to achieve surgical strike.

Key words: ship target, critical part, similarity-based attention module, feature fusion, precise choice

CLC Number:

E 919

Yao WANG, Huiqi XU, Silei CAO, Lei WANG. Detection algorithm of ship critical parts based on deep learning[J]. Systems Engineering and Electronics, 2026, 48(2): 410-421.

Figures/Tables 20

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References 41

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骨干网络	AP/%	FPS
ResNet-18	28.1	142
DLA-34	37.4	52
Hourglass-104	45.1	1.4

注意力机制	参数量
SE^[29]	$ 2{C}^{{{}^{2}}}/r $
CBAM^[30]	$ 2{C}^{{{}^{2}}}/r+2{k}^{2} $
ECA^[31]	$ k $
SimAM	0

模型	Top-1- 准确率/%	Top-5-准确率/%	参数量/%	参数增加量/（×10⁶）	每秒浮点运算次数（十亿次/秒）	FPS
ResNet-18	70.33	89.58	11.69	0	1.82	215
+ SE	71.19	90.21	11.78	0.087	1.82	144
+ CBAM	71.24	90.04	11.78	0.090	1.82	78
+ ECA	70.71	89.85	11.69	36	1.82	148
+ SimAM	71.31	89.88	11.69	0	1.82	147
ResNet-34	73.75	91.60	21.80	0	3.67	119
+ SE	74.32	91.99	21.95	0.157	3.67	81
+ CBAM	74.41	91.85	21.96	0.163	3.67	38
+ ECA	74.03	91.73	21.81	74	3.67	82
+ SimAM	74.46	92.02	21.80	0	3.67	78
ResNet-101	77.82	93.85	44.55	0	7.83	47
+ SE	78.39	94.13	49.29	4.743	7.85	33
+ CBAM	78.57	94.18	49.33	4.781	7.85	14
+ ECA	78.46	94.12	44.55	171	7.84	33
+ SimAM	78.65	94.11	44.55	0	7.83	32
MobileNetV2	71.90	90.51	3.50	0	0.31	99
+ SE	72.46	90.85	3.53	0.028	0.31	65
+ CBAM	72.49	90.78	3.54	0.032	0.32	35
+ ECA	72.01	90.46	3.50	59	0.31	66
+ SimAM	72.36	90.74	3.50	0	0.31	66

关键部位	数量
驾驶舱	6945
天线桅杆	7197
雷达	3237
水线	8158

参数	取值
迭代次数	200
学习率	1.25×10⁻⁴
输入图像尺寸	512×512
批训练规模	8

Detection algorithm of ship critical parts based on deep learning

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标注方式	雷达	驾驶舱	天线桅杆	水线
旋转框标注方式	73.59	89.72	67.03	83.14
水平框标注方式	68.73	85.14	64.05	76.93

网络	mAP/%	FPS
Mask R-CNN^[36]	70.53	17.1
ResNet18^[37]	67.42	30.2
DLASeg^[25]	71.33	39.5
Hourglass^[38]	73.27	12.6
YOLOv4^[39]	65.48	37.5
YOLOv8^[40]	69.19	35.9
本文算法	76.91	36.7

网络结构	DLA	RFB	SimAM	DLASeg	参数量/（×10⁶）	运算量/（×10⁶）
Mask R-CNN	—	—	—	—	697.93	1093872.55
YOLOv4	—	—	—	—	89.14	156754.03
YOLOv8	—	—	—	—	116.32	187925.54
本文算法*	√	—	—	—	84.39	155866.69
本文算法**	√	√	—	—	84.72	161076.01
本文算法***	√	—	√	—	84.39	155195.61
本文算法****	√	—	—	√	67.91	95762.35
本文算法*****	√	√	√	√	74.06	104019.14

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