[1]王墨川,王 波,王熊珏,等. 基于高语义特征与注意力机制的桥梁裂缝检测[J].湖北工业大学学报,2022,(2):55-59.
　WANG Mochuan,WANG Bo,WANG Xiongjue,et al. Bridge Crack Detection Based on High Level Semantic Feature and Attention Mechanism[J].,2022,(2):55-59.
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 基于高语义特征与注意力机制的桥梁裂缝检测()
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《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
期数:
2022年第2期
页码:
55-59
栏目:
湖北工业大学学报
出版日期:
2022-04-29

文章信息/Info

Title:
 Bridge Crack Detection Based on High Level Semantic Feature and Attention Mechanism
文章编号:
1003-4684(2022)02-0055-05
作者:
 王墨川1;  王 波2; 3;  王熊珏2; 3;  夏文祥2; 3;  阮小丽2; 3
 1 湖北工业大学电气与电子工程学院， 湖北 武汉 430068；
 2 桥梁结构健康与安全国家重点实验室，湖北 武汉 430034;
 3 中铁大桥科学研究院有限公司，湖北 武汉 430034
Author(s):
 WANG Mochuan1;  WANG Bo2; 3;  WANG Xiongjue2; 3;  XIA Wenxiang2; 3;  RUAN Xiaoli2; 3
 1 School of Electrical and Electronic Engineering, Hubei Univ. of Tech., Wuhan 430068, China;
 2 State Key Laboratory for Health and Safety of Bridge Structures, Wuhan 430034, China;
 3 China Railway Bridge Science Research Institute, Ltd., Wuhan 430034, China
关键词:
 ; 高语义特征;  注意力机制;  桥梁裂缝检测
Keywords:
 ; high-level semantic feature;  attention mechanism;  bridge crack detection
分类号:
TP23， TP181， U8
文献标志码:
A
摘要:
 为提高桥梁裂缝的检测效果，提出了一种基于高语义特征与注意力机制的桥梁裂缝检测（deep bridge crack detection，DBCD）网络。该网络基于编码器-解码器网络架构，首先编码器基于SegNet，在下采样过程中引入最大池化索引，以避免提取桥梁裂缝特征时细节损失；其次在编码器和解码器间加入高语义特征融合模块，提高检测不同桥梁裂缝尺度的鲁棒性；再次在解码器阶段引入注意力机制，设计基于混合域注意力机制的解码器模块，实现对桥梁裂缝的准确定位。最后在现场真实拍摄的桥梁裂缝图像数据集上，与传统的网络进行了比较，DBCB具有更丰富的细节、更准确的定位及更高的检测精度。
Abstract:
 In order to improve the detection effect of bridge cracks, a Deep bridge crack detection (DBCD) networks based on high level semantic feature and attention mechanism is proposed. The detection networks is based on the encoder decoder network architecture. Firstly, the encoder is based on SegNet, and the maximum pool index is introduced in the downsampling process, which can avoid the loss of details when extracting bridge crack features. Secondly, by adding a high level semantic feature fusion (HSFF) module between the encoder and the decoder, the robustness of detecting different bridge crack scales is improved. Subsequently, by introducing an attention mechanism in the decoder stage, designing a Hybrid domain attention mechanism (HDAM) to achieve accurate positioning of bridge cracks. Finally, compared with the traditional model, DBCB has richer details, more accurate positioning and higher detection accuracy by using it on the real bridge crack image data set taken on site.

参考文献/References:

[1] ZOU Q, ZHANG Z, LI Q, et al. Deepcrack: Learning hierarchical convolutional features for crack detection[J]. IEEE Transactions on Image Processing, 2018, 28(3): 1498-1512.
[2] XIE S, TU Z. Holistically-nested edge detection[C]∥Proceedings of the IEEE International Conference on Computer Vision. 2015: 1395-1403
[3] MANINIS K K, PONT-TUSET J, ARBELáEZ P, et al. Convolutional oriented boundaries[C]∥European Conference on Computer Vision. Springer, Cham, 2016: 580-596.
[4] KHOREVA A, BENENSON R, OMRAN M, et al. Weakly supervised object boundaries[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 183-192.
[5] KOROT E, GUAN Z, FERRAZ D, et al. Code-free deep learning for multi-modality medical image classification[J]. Nature Machine Intelligence, 2021, 3(4): 288-298.
[6] ISENSEE F, JAEGER P F, KOHL S A A, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[J]. Nature methods, 2021, 18(2): 203-211.
[7] SANTHANAVIJAYAN A, KUMAR D N, DEEPAK G. A semantic-aware strategy for automatic speech recognition incorporating deep learning models[M]∥ Singapore： Springer, 2021: 247-254.
[8] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 40(4): 834-848.
[9] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 3431-3440.
[10] RONNEBERGER O, FISCHER P, BROX T. U-net: Convolutional networks for biomedical image segmentation[C]∥International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2015: 234-241.
[11] GU Z, CHENG J, FU H, et al. Ce-net: Context encoder network for 2d medical image segmentation[J]. IEEE Transactions on Medical Imaging, 2019, 38(10): 2281-2292.
[12] CHEN T, CAI Z, ZHAO X, et al. Pavement crack detection and recognition using the architecture of segNet[J]. Journal of Industrial Information Integration, 2020, 18: 100144.
[13] 曹锦纲,杨国田,杨锡运.基于注意力机制的深度学习路面裂缝检测[J].计算机辅助设计与图形学学报,2020,32(08):1324-1333.
[14] WOO S, PARK J, LEE J Y, et al. Cbam: Convolutional block attention module[C]∥Proceedings of the European Conference on Computer Vision (ECCV). 2018: 3-19.
[15] PASZKE A, GROSS S, MASSA F, et al. Pytorch: an imperative style, high-performance deep learning library[J]. Advances in Neural Information Processing Systems, 2019, 32: 8026-8037.
[16] BADRINARAYANAN V, KENDALL A, CIPOLLA R. Segnet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481-2495.
[17] FU J, LIU J, TIAN H, et al. Dual attention network for scene segmentation[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 3146-3154.
[18] ZHAO H, SHI J, QI X, et al. Pyramid scene parsing network[C]∥Proceedings of the IEEE cOnference on Computer Vision and Pattern Recognition. 2017: 2881-2890.
[19] OLIVEIRA H, CORREIA P L. CrackIT—An image processing toolbox for crack detection and characterization[C]∥2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014: 798-802.
[20] LI G, LI X, ZHOU J, et al. Pixel-level bridge crack detection using a deep fusion about recurrent residual convolution and context encoder network[J]. Measurement, 2021, 176: 109171.

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备注/Memo

备注/Memo:
［收稿日期］ 2021-10-08
［第一作者］ 王墨川（1996-），男，湖北宜昌人， 湖北工业大学硕士研究生，研究方向为图像处理与模式识别
［通信作者］ 王   波（1980-），男，湖北宜昌人， 工学博士，中铁大桥科学研究院有限公司教授级高工，研究方向为桥梁结构振动控制，桥梁智能建造
更新日期/Last Update: 2022-04-28