Fracture Zone Extraction Method Based on Three-Dimensional Convolutional Neural Network Combined with PointSIFT

Fig. 2. Schematic diagram of OE convolution unit. (a) Point cloud in 3D space (the input point is at origin); (b) nearest neighbour search in eight octants; (c) convolution along X, Y, Z axis

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Fig. 3. PointSIFT module

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Fig. 4. Schematic diagram of three-dimensional convolution module framework

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$Schematic diagram of PS-CNN point cloud fracture zone extraction framework$

Fig. 5. Schematic diagram of PS-CNN point cloud fracture zone extraction framework

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Fig. 6. Sample display diagrams of ISPRS point cloud datasets. (a); Samp51; (b) Samp53

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Fig. 7. Sample display diagrams of Chuandian point cloud datasets. (a) CD_1; (b) CD_2

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$Results of three fracture zone extraction methods on Samp51. (a) Label; (b) TD; (c) DNN; (d) PS-CNN$

Fig. 8. Results of three fracture zone extraction methods on Samp51. (a) Label; (b) TD; (c) DNN; (d) PS-CNN

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$Results of three fracture zone extraction methods on Samp53. (a) Label; (b) TD; (c) DNN; (d) PS-CNN$

Fig. 9. Results of three fracture zone extraction methods on Samp53. (a) Label; (b) TD; (c) DNN; (d) PS-CNN

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$Results of three fracture zone extraction methods on CD_1. (a) Label; (b) TD; (c) DNN; (d) PS-CNN$

Fig. 10. Results of three fracture zone extraction methods on CD_1. (a) Label; (b) TD; (c) DNN; (d) PS-CNN

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$Results of three fracture zone extraction methods on CD_2. (a) Label; (b) TD; (c) DNN; (d) PS-CNN$

Fig. 11. Results of three fracture zone extraction methods on CD_2. (a) Label; (b) TD; (c) DNN; (d) PS-CNN

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Table 1. Confusion matrix of classification results and evaluation errors calculation method
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Table 1. Confusion matrix of classification results and evaluation errors calculation method
Error Calculation method
T.I c/（c+d）
T.II b/（a+b）
T.E. （b+c）/（a+b+c+d）

Table 2. Performance comparison of three fracture zone extraction methods on ISPRS dataset

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Table 2. Performance comparison of three fracture zone extraction methods on ISPRS dataset

Dataset	Method	T.Ⅰ/%	T.Ⅱ/%	T.E./%	Accuracy /%	Time /s
Samp51	TD	1.22	9.31	2.82	97.18	16
	DNN	0.46	4.95	1.35	98.65	32
	PS-CNN	0.40	2.43	0.79	99.21	51
Samp53	TD	2.43	17.37	3.83	96.17	99
	DNN	1.07	11.39	2.03	97.97	136
	PS-CNN	0.18	10.16	1.11	98.89	193

Table 3. Performance comparison of three fracture zone extraction methods on Chuandian dataset

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Table 3. Performance comparison of three fracture zone extraction methods on Chuandian dataset

Dataset	Method	T.Ⅰ/%	T.Ⅱ/%	T.E./%	Accuracy /%	Time /s
CD_1	TD	1.15	1.47	1.32	98.68	238
	DNN	0.77	0.97	0.88	99.12	342
	PS-CNN	0.29	0.49	0.40	99.60	424
CD_2	TD	1.39	1.31	1.36	98.64	273
	DNN	0.63	2.01	1.28	98.72	364
	PS-CNN	0.21	0.50	0.35	99.65	473

Table 4. Performance comparison of three fracture zone extraction methods on ISPRS dataset
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Table 4. Performance comparison of three fracture zone extraction methods on ISPRS dataset
Dataset PointSIFT T.Ⅰ /% T.Ⅱ /% T.E. /% Aaccuracy /%
Samp51 0.44 2.48 0.83 99.17
√ 0.40 2.43 0.79 99.21
Samp53 0.23 10.27 1.17 98.83
√ 0.18 10.16 1.11 98.89

Table 5. Performance comparison of the proposed method on point cloud samples with different dilution rate
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Table 5. Performance comparison of the proposed method on point cloud samples with different dilution rate
Dataset Method Dilution rate Point Aaccuracy /% Time /s
CD_1 PS-CNN 0.3 65817 99.58 370
0.2 75363 99.60 424
0.1 84670 99.63 476
CD_2 PS-CNN 0.3 72357 99.64 412
0.2 83059 99.65 473
0.1 93434 99.67 532

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Hao Wang, Dongmei Song, Bin Wang, Song Dai. Fracture Zone Extraction Method Based on Three-Dimensional Convolutional Neural Network Combined with PointSIFT[J]. Laser & Optoelectronics Progress, 2023, 60(24): 2410011

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Paper Information

Category: Image Processing

Received: Feb. 27, 2023

Accepted: May. 15, 2023

Published Online: Dec. 4, 2023

The Author Email: Hao Wang (z20160115@s.upc.edu.cn)

DOI:10.3788/LOP230737

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Confusion matrix of classification results and evaluation errors calculation method

Table 1. Confusion matrix of classification results and evaluation errors calculation method

Table 2. Performance comparison of three fracture zone extraction methods on ISPRS dataset

Table 2. Performance comparison of three fracture zone extraction methods on ISPRS dataset

Table 3. Performance comparison of three fracture zone extraction methods on Chuandian dataset

Table 3. Performance comparison of three fracture zone extraction methods on Chuandian dataset

Table 4. Performance comparison of three fracture zone extraction methods on ISPRS dataset

Table 4. Performance comparison of three fracture zone extraction methods on ISPRS dataset

Table 5. Performance comparison of the proposed method on point cloud samples with different dilution rate

Table 5. Performance comparison of the proposed method on point cloud samples with different dilution rate