基于注意力解码和连续性监督的裂缝检测方法

doi:10.3778/j.issn.1002-8331.2309-0292

摘要/Abstract

摘要： 裂缝检测是预防重大建筑坍塌事故的重要措施。针对裂缝特征微弱和图像背景噪声干扰较多的问题，引入语义分割领域的编码解码结构算法来提升检测鲁棒性。为了解决编码解码结构网络中解码方式单一且不能高效联系编码特征语义信息的缺陷，设计了基于注意力机制的解码模块，通过注意力机制增强解码器对裂缝特征的关注度，优化了模型的解码效果；对于模型预测结果中裂缝断裂问题，改进了连续性监督算法，通过在模型预测阶段加入连续性邻接图预测，并结合对应的损失函数进行监督，提高预测结果中裂缝的特征连续性；最后结合这两种方法搭建了一种编码解码结构的自动化裂缝检测模型。通过在两个数据集上的实验结果，验证了所提方法的优越性，在选取的常用算法中取得了最高的检测精度。

关键词: 裂缝检测, 语义分割, 编码解码, 注意力机制, 连续性监督

Abstract: Crack detection is an important measure to prevent building collapse accidents. Aiming at the weak crack feature and interference from noise in crack images, encoder-decoder nets in semantic segmentation are introduced to improve the detection robustness. An attention decoder module which enhances the attention of the crack feature in decoder through attention mechanism is designed to address the shortcomings of the simple decoder in traditional encoder-decoder nets and the low efficiency of connecting the semantic information in encoding features. For the problem of cracks fracture in predictions, the continuous supervision algorithm is improved, by adding adjacency connected predictions and combining the corresponding loss function for supervision, the continuity of crack features in prediction results is improved. Combining these two methods, an automatic encoder-decoder crack detection model is proposed. The experimental results on two datasets verify the superiority of the proposed model, the best detection accuracy is achieved among the commonly used models selected.

Key words: crack detection, semantic segmentation, encoder-decoder, attention mechanism, continuous supervision

谢永华, 卓安南. 基于注意力解码和连续性监督的裂缝检测方法[J]. 计算机工程与应用, 2025, 61(4): 122-129.

XIE Yonghua, ZHUO Annan. Crack Detection Method Based on Attention Decoder and Continuous Supervision[J]. Computer Engineering and Applications, 2025, 61(4): 122-129.

参考文献

[1] ZHANG L, YANG F, ZHANG Y D, et al. Road crack detection using deep convolutional neural network[C]//Proceedings of the 2016 IEEE International Conference on Image Processing. Piscataway: IEEE, 2016: 3708-3712.
[2] HUANG H W, LI Q T, ZHANG D M, et al. Deep learning based image recognition for crack and leakage defects of metro shield tunnel[J]. Tunnelling and Underground Space Technology, 2018, 77: 166-176.
[3] LIAO J H, YUE Y H, ZHANG D J, et al. Automatic tunnel crack inspection using an efficient mobile imaging module and a lightweight CNN[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 15190-15203.
[4] 徐志刚, 赵祥模, 宋焕生, 等. 基于直方图估计和形状分析的沥青路面裂缝识别算法[J]. 仪器仪表学报, 2010, 31(10): 2260-2266.
XU Z G, ZHAO X M, SONG H S, et al. Asphalt pavement crack recognition algorithm based on histogram estimation and shape analysis[J]. Chinese Journal of Scientific Instrument, 2010, 31(10): 2260-2266.
[5] LIM R S, LA F M, SHENG W. A robotic crack inspection and apping system for bridge deck maintenance[J]. IEEE Transactions on Automation Science and Engineering, 2014, 11(2): 367-378.
[6] ABDEL-QADER L, ABUDAYYEH O, KELLY M E, et al. Analysis of edge-detection techniques for crack identification in bridges[J]. Journal of Computing in Civil Engineering, 2003, 17(4): 255-263.
[7] PENG C, YANG M Q, ZHENG Q H, et al. A triple-thresholds pavement crack detection method leveraging random structured forest[J]. Construction and Building Materials, 2020, 263: 950-618.
[8] SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39: 640-651.
[9] BADRINARAYANAN V, KENDALL A, CIPOLLA R. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39: 2481-2495.
[10] RONNEBERGER O, FISCHER P, BROX T. UNet: convo-lutional networks for biomedical image segmentation[C]//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015: 234-241.
[11] CHEN L C, PAPANDREOU I, KOKKINOS K, 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, 2018, 40: 834-848.
[12] ZOU Q, ZHANG Z, LI Q, et al. DeepCrack: learning hierarchical convolutional features for crack detection[J]. IEEE Transactions on Image Processing, 2019, 28: 1498-1512.
[13] YANG F, ZHANG L, YU S, et al. Feature pyramid and hierarchical boosting network for pavement crack detection[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21: 1525-1535.
[14] DONG H, SONG K, HE Y, et al. PGA-Net: pyramid feature fusion and global context attention network for automated surface defect detection[J]. IEEE Transactions on Industrial Informatics, 2019, 16: 7448-7458.
[15] 常惠, 绕志强, 赵玉林, 等. 基于改进U-Net网络的隧道裂缝分割算法研究[J]. 计算机工程与应用, 2021, 57(22): 215-222.
CHANG H, RAO Z Q, ZHAO Y L, et al. Research on tunnel crack segmentation algorithm based on improved U-Net network[J]. Computer Engineering and Applications, 2021, 57(22): 215-222.
[16] GUO J M, MARKONI H, LEE I D, et al. BARNet: boundary aware refinement network for crack detection[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23: 7343-7358.
[17] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 6000-6010.
[18] WANG H, CAO P, WANG J, et al. UCTransNet: rethinking the skip connections in U-Net from a channel-wise perspective with transformer[C]//Proceedings of the 36th AAAI Conference on Artificial Intelligence, 2022: 2441-2449.
[19] WANG F, JIANG M, QIAN C, et al. Residual attention network for image classification[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 6450-6458.
[20] ZHANG D, ZHENG Z, LI M, et al. CSART: channel and spatial attention-guided residual learning for real-time object tracking[J]. Neurocomputing, 2021, 436: 260-272.
[21] ZHOU Q, QU Z, LI Y, et al. Tunnel crack detection with linear seam based on mixed attention and multiscale feature fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 5014711.
[22] OJALA T, PIETIKAAINEN M, HARWOOD D. A comparative study of texture measures with classification based on featured distributions[J]. Pattern Recognition, 1996, 21: 51-59.
[23] ZHOU W J, FEI M R, ZHOU H Y, et al. A sparse representation based fast detection method for surface detect detection of bottle caps[J]. Neurocomputing, 2014, 123: 406-414.
[24] LIU X, XUE F, TENG L. Surface defect detection based on gradient LBP[C]//Proceedings of the 2018 IEEE 3rd International Conference on Image, Vision and Computing. Piscataway: IEEE, 2018: 133-137.
[25] OKTAY O, SCHLEMPER J, FOLGOC L. Attention U-Net: learning where to look for the pancreas[J]. arXiv:1804.03999, 2018.