Citation: | LI Yunhui, KANG Shuai, HE Dongqing, Ding Yapeng. Quantitative Evaluation of Crack Damage Based on U2-Net and Morphology[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(11): 67-77. doi: 10.3724/j.gyjzG23110710 |
[1] |
RAFIEI M H, ADELI H. A novel machine learning-based algorithm to detect damage in high-rise building structures [J]. The Structural Design of Tall and Special Buildings, 2017, 26(18), e1400.
|
[2] |
GRAYBEAL B A, PHARES B M, ROLANDER D D, et al. Visual inspection of highway bridges [J]. Journal of Nondestructive Evaluation, 2002, 21(3): 67-83.
|
[3] |
刘宇飞, 樊健生, 聂建国, 等.结构表面裂缝数字图像法识别研究综述与前景展望[J]. 土木工程学报, 2021, 54(6): 79-98.
|
[4] |
HSIEH Y A, TSAI Y J. Machine learning for crack detection: review and model performance comparison [J]. Journal of Computing in Civil Engineering, 2020, 34(5), 04020038.
|
[5] |
LIU Y, YAO J, LU X, et al. Deep Crack: a deep hierarchical feature learning architecture for crack segmentation [J]. Neurocomputing, 2019, 338: 139-153.
|
[6] |
DUNG C V. Autonomous concrete crack detection using deep fully convolutional neural network [J]. Automation in Construction, 2019, 99: 52-58.
|
[7] |
李伟, 申浩, 马志丹, 等. 基于深度卷积网络的路面裂缝分割方法[C]//2019世界交通运输大会论文集(下). 北京:2019: 346-356.
|
[8] |
杨明, 刘露, 郭愫愫. 机器视觉在船舶焊缝图像缺陷分割检测中的应用[J]. 舰船科学技术, 2021, 43(18): 217-219.
|
[9] |
李怡静, 程浩东, 李火坤, 等. 基于改进U2-Net与迁移学习的无人机影像堤防裂缝检测[J]. 水利水电科技进展, 2022, 42(6): 52-59.
|
[10] |
王盛, 吴浩, 彭宁, 等. 改进U2-Net的太阳能电池片缺陷分割方法[J]. 国外电子测量技术, 2023, 42(2): 177-184.
|
[11] |
FAN Z, WU Y, LU J, et al. Automatic pavement crack detection based on structured prediction with the convolutional neural network[EB/OL].[2018-02-01]. https://doi.org/10.48550/arXiv.1803.02208.
|
[12] |
章世祥, 张汉成, 李西芝, 等. 基于机器视觉的路面裂缝病害多目标识别研究[J]. 公路交通科技, 2021, 38(3): 30-39.
|
[13] |
谭云峰, 刘昆, 莫洪柳, 等. 基于优化U-Net的路面裂缝分割及其影响因素研究[J]. 公路交通科技, 2023, 40(12): 17-25.
|
[14] |
YU J, WU C, LI Y, et al. Intelligent identification of coal crack in CT images based on deep learning[J/OL]. Computational Intelligence and Neuroscience, 2022[2023-11-07]. https://doi.org/10.1155/2022/7092436.
|
[15] |
WONG K C L, MORADI M, TANG H, et al. 3D segmentation with exponential logarithmic loss for highly unbalanced object sizes[C]//Medical Image Computing and Computer Assisted Intervention-MICCAI 2018. Granada: 2018: 612-619.
|
[16] |
彭博, 蒋阳升, 蒲云. 基于数字图像处理的路面裂缝自动分类算法[J]. 中国公路学报, 2014, 27(9): 10-18
, 24.
|
[17] |
LI S, ZHAO X, ZHOU G. Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network [J]. Computer-Aided Civil and Infrastructure Engineering, 2019, 34(7): 616-634.
|
[18] |
丁威, 俞珂, 舒江鹏. 基于深度学习和无人机的混凝土结构裂缝检测方法[J]. 土木工程学报, 2021, 54(增刊1): 1-12.
|
[19] |
YANG X, LI H, YU Y, et al. Automatic pixel-level crack detection and measurement using fully convolutional network [J]. Computer-Aided Civil and Infrastructure Engineering, 2018, 33(12): 1090-1109.
|
[20] |
QIN X, ZHANG Z, HUANG C, et al. U2-Net: going deeper with nested U-structure for salient object detection [J]. Pattern Recognition, 2020, 106, 107404.
|
[21] |
CHENG H, LI Y, LI H, et al. Embankment crack detection in UAV images based on efficient channel attention U2Net[J].Structures, 2023, 50: 430-443.
|
[22] |
GUO Y, SHI L, ZHANG J. U2-Net: a stacked and nested network with axial attention for detection of building surface cracks[C]//2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta). Haikou, China: 2022: 1292-1297.
|
[23] |
ZHENG Z, YANG K. Wall crack detection method based on improved YOLOv5 and U2-Net[J]. International Journal of Wireless and Mobile Computing, 2023, 25(4): 362-367.
|
[24] |
陈其浩, 孙林, 张倩. 基于改进U2-Net的透明件划痕检测方法[J]. 科学技术与工程, 2022, 22(2): 620-627.
|
[25] |
孙佳佳, 李承礼, 常德显, 等. 基于生成对抗网络的入侵检测类别不平衡问题数据增强方法[J]. 科学技术与工程, 2022, 22(18): 7965-7971.
|
[26] |
LIU Y, YAO J, LU X, et al. DeepCrack: a deep hierarchical feature learning architecture for crack segmentation [J]. Neurocomputing, 2019, 338: 139-153.
|
[27] |
LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: 2015: 3431-3440.
|
[28] |
CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of the European Conference on Computer Vision (ECCV). Munich, Germany: 2018: 801-818.
|
[29] |
RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015. Munich, Germany: 2015:234-241.
|
[30] |
于锦程. 基于深度学习的桥梁表观裂缝量化识别研究[D]. 大连: 大连理工大学, 2022.
|