APPLICATION OF AN IMPROVED U-NET NEURAL NETWORK ON FRACTURE SEGMENTATION FROM OUTCROP IMAGES

被引：7

作者：

Wang, Zhibao ^{[1
,2
]}

Zhang, Ziming ^{[1
]}

Bai, Lu ^{[3
]}

Yang, Yuze ^{[1
]}

Ma, Qiang ^{[4
]}

机构：

[1] Northeast Petr Univ, Sch Comp & Informat Technol, Daqing, Peoples R China

[2] Northeast Petr Univ, Bohai Rim Energy Res Inst, Qinhuangdao, Hebei, Peoples R China

[3] Ulster Univ, Sch Comp, Belfast, Antrim, North Ireland

[4] Heilongjiang Bayi Agr Univ, Coll Informat & Elect Engn, Daqing, Peoples R China

来源：

2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022) | 2022年

关键词：

Deep learning; outcrop; fracture detection; ResNeXt; U-Net;

D O I：

10.1109/IGARSS46834.2022.9883208

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Outcrop records contain very rich geological historical information, and the study of fractures in outcrop areas is an important part of geological exploration work. The accurate fracture information can provide useful technical support for the development and exploration of subsurface oil and gas. The outcrop images usually include unclear boundaries, complex structure and inconspicuous features, which make fracture detection from outcrop images a difficult task. To tackle these challenges, an improved U-Net algorithm based on the ResNeXt module is proposed in this paper to segment the fractures from the outcrop images. Experiments are conducted on the outcrop images from Yijianfang area in the Tarim Basin in China, and the results show that the proposed algorithm has improved the accuracy and IoU in fracture segmentation from the outcrop images.

引用

页码：3512 / 3515

页数：4

共 18 条

[1] SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation [J].

Badrinarayanan, Vijay ;

Kendall, Alex ;

Cipolla, Roberto .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017, 39 (12) :2481-2495

[2] A COMPUTATIONAL APPROACH TO EDGE-DETECTION [J].

CANNY, J .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1986, 8 (06) :679-698

[3] Split-and-merge algorithms defined on topological maps for 3D image segmentation [J].

Damiand, G ;

Resch, P .

GRAPHICAL MODELS, 2003, 65 (1-3) :149-167

[4] Deep Residual Learning for Image Recognition [J].

He, Kaiming ;

Zhang, Xiangyu ;

Ren, Shaoqing ;

Sun, Jian .

2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778

[5] Novel precision target detection with adaptive thresholding for dynamic image segmentation [J].

Kim, BG ;

Kim, DJ ;

Park, DJ .

MACHINE VISION AND APPLICATIONS, 2001, 12 (05) :259-270

[6]

Long J, 2015, PROC CVPR IEEE, P3431, DOI 10.1109/CVPR.2015.7298965

[7] DEEP LEARNING APPLICATION FOR FRACTURE SEGMENTATION OVER OUTCROP IMAGES FROM UAV-BASED DIGITAL PHOTOGRAMMETRY [J].

Marques, Ademir, Jr. ;

Racolte, Graciela ;

de Souza, Eniuce Menezes ;

Domingos, Hiduino Venancio ;

Horota, Rafael Kenji ;

Motta, Joao Gabriel ;

Zanotta, Daniel Capella ;

Cazarin, Caroline Lessio ;

Gonzaga, Luiz, Jr. ;

Veronez, Mauricio Roberto .

2021 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM IGARSS, 2021, :4692-4695

[8]

Pringle J., 2004, GEOL TODAY, V20, P67, DOI [DOI 10.1111/J.1365-2451.2004.00450.X, 10.1111/j.1365-2451.2004.00450.x]

[9] U-Net: Convolutional Networks for Biomedical Image Segmentation [J].

Ronneberger, Olaf ;

Fischer, Philipp ;

Brox, Thomas .

MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION, PT III, 2015, 9351 :234-241

[10] Detection of oil wells based on Faster R-CNN in optical satellite remote sensing images [J].

Song, Guangfu ;

Wang, Zhibao ;

Bai, Lu ;

Zhang, Jie ;

Chen, Liangfu .

IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING XXVI, 2020, 11533

← 1 2 →