Ship Classification Method for Point Cloud Images Based on Three-Dimensional Convolutional Neural Network

Yongmei Ren; Jie Yang; Zhiqiang Guo; Yilei Chen

doi:10.3788/LOP57.161022

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 16 >Page 161022 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 16, 161022 (2020)

Ship Classification Method for Point Cloud Images Based on Three-Dimensional Convolutional Neural Network

Yongmei Ren^1、2, Jie Yang^1、*, Zhiqiang Guo¹, and Yilei Chen³

Author Affiliations

¹Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei 430070, China

²School of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, Hunan 421002, China

³School of Artificial Intelligence, Xidian University, Xi'an, Shaanxi 710071, China

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DOI: 10.3788/LOP57.161022 Cite this Article Set citation alerts

Yongmei Ren, Jie Yang, Zhiqiang Guo, Yilei Chen. Ship Classification Method for Point Cloud Images Based on Three-Dimensional Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(16): 161022 Copy Citation Text

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Fig. 1. Flow chart of proposed ship classification model

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Fig. 2. Structural diagram of typical CNN

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Fig. 3. Network structure of 3D CNN

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Fig. 4. CAD data and point cloud ship images. (a)(b) Cabin; (c)(d) rowing; (e)(f) sailing; (g)(h) cruise; (i)(j) cargo

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Fig. 5. Point cloud data samples in Sydney urban object dataset

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Layer	Input size	Filter size	Stride	Output size	Number of parameters
Conv1	32×32×32×1	5×5×5×32	2	14×14×14×32	4032
Conv2	14×14×14×32	3×3×3×32	1	12×12×12×32	27680
Conv3	12×12×12×32	3×3×3×64	1	10×10×10×64	55360
Max Pooling 1	10×10×10×64	2×2×2	2	5×5×5×64	0
FC1	5×5×5×64	-	-	512	4096001
FC2	512	-	-	128	65537
FC3-Softmax	128	-	-	5	641

Table 1. Detail parameters of 3D CNN

No.	Class	Number of samplesin training set	Number of samplesin testing set
1	Cabin	231	57
2	Rowing	231	57
3	Sailing	231	57
4	Cruise	231	57
5	Cargo	231	57
Total		1155	285

Table 2. Numbers of training and testing samples in self-build point cloud image ship dataset

No.	Class	Number of samplesin training set	Number of samplesin testing set
1	Cabin	116	28
2	Rowing	116	28
3	Sailing	116	28
4	Cruise	116	28
5	Cargo	116	28
Total		580	140

Table 3. Numbers of training and testing samples in ship dataset of point cloud images without noise

Size	32×32×32	48×48×48
Accuracy /%	97.14	95.71

Table 4. Classification accuracy of proposed 3D CNN model under each size of voxel grid

Method	Accuracy /%	F1-score	Training time
PFH+BoW+SVM	96.43	0.9669	2.95 h
3D ShapeNets	90.71	0.9056	32.63 s
VoxNet	95.00	0.9500	13.44 s
Method in Ref. [17]	95.71	0.9566	62.25 s
Proposed method	97.14	0.9714	15.88 s

Table 5. Classification accuracy, F1-score, and training time of each method on ship dataset of point cloud images without noise

Method	Accuracy /%	F1-score	Training time/s
3D ShapeNets	90.17	0.9006	64.91
VoxNet	93.68	0.9358	28.01
Method in Ref. [17]	94.73	0.9471	144.47
Proposed method	96.14	0.9613	32.91

Table 6. Classification accuracy, F1-score, and training time of each method on self-built point cloud image ship dataset

Method	Accuracy /%	F1-score	Training time/s
GFH+SVM^[12]	73.58	-	-
VoxNet	89.51	0.8939	77.11
Method in Ref. [15]	84.00	-	-
Method in Ref. [17]	87.37	0.8661	445.05
Proposed method	91.58	0.9153	90.45

Table 7. Classification accuracy, F1-score, and training time of each method on Sydney urban object dataset

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