Hướng tới cải thiện độ chính xác phân đoạn nứt bê tông trong các tình huống phức tạp: một mạng U-Net điều chỉnh mới

Feng Qu1,2, Bokun Wang1,2, Qing Zhu1,2, Fu Xu3, Yaojing Chen4, Caiqian Yang1,2
1School of Civil Engineering, Southeast University, Nanjing, China
2Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing, China
3School of Civil Engineering, Xiangtan University, Xiangtan, China
4Jiangsu Expressway Engineering Maintenance Co., Ltd, Huai’an, China

Tóm tắt

Mạng nơ-ron tích chập (CNN) đã cho thấy độ chính xác hứa hẹn trong việc phân đoạn các vết nứt bê tông dưới các điều kiện kiểm soát. Tuy nhiên, các phương pháp hiện có thường gặp khó khăn trong việc xử lý các mẫu đa quy mô hoặc các tình huống phức tạp. Để giải quyết vấn đề phổ biến này, một mạng U-Net đã được điều chỉnh trong nghiên cứu hiện tại. Mô hình được đề xuất, mang tên U-Net-ASPP-CBAM, tích hợp mô-đun chú ý khối tích chập sau một phép toán tích chập để tập trung chọn lọc vào thông tin về vết nứt trong khi bỏ qua các chi tiết nền không liên quan trong quá trình trích xuất đặc trưng. Hơn nữa, mạng U-Net-ASPP-CBAM thay thế một lớp gộp bằng mô-đun gộp theo tháp không gian atrous để khám phá và kết hợp các đặc trưng trên nhiều quy mô, nâng cao khả năng nắm bắt thông tin cho việc phân đoạn các đối tượng nhỏ. Hiệu suất của mô hình được đề xuất đã được xác thực bởi một tập dữ liệu tự xây dựng bao gồm các hình ảnh vết nứt với nhiều nền phức tạp khác nhau. Và hiệu quả phân đoạn được đánh giá thông qua các chỉ số đánh giá, bao gồm độ chính xác, độ hồi tưởng, điểm F1, độ chính xác pixel và trung bình giao nhau trên hợp nhất. Kết quả cho thấy mô hình U-Net-ASPP-CBAM proposed vượt trội hơn so với các mô hình phân đoạn khác.

Từ khóa

#mạng nơ-ron tích chập #phân đoạn vết nứt bê tông #U-Net #mô-đun chú ý khối tích chập #gộp theo tháp không gian atrous

Tài liệu tham khảo

Shamsabadi EA, Xu C, Rao AS, Nguyen T, Ngo T, Dias-da-Costa D (2022) Vision transformer-based autonomous crack detection on asphalt and concrete surfaces. Auto Constr 140:104316. https://doi.org/10.1016/j.autcon.2022.104316 Yamaguchi T, Hashimoto S (2009) Fast crack detection method for large-size concrete surface images using percolation-based image processing. Inter Mach Visi Appli 21(5):797–809. https://doi.org/10.1007/s00138-009-0189-8 Nhat-Duc H, Nguyen Q-L, Tran V-D (2018) Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network. Auto Constr 94:203–213. https://doi.org/10.1016/j.autcon.2018.07.008 Arena A, Delle Piane C, Sarout J (2014) A new computational approach to cracks quantification from 2D image analysis: Application to micro-cracks description in rocks. Comp Geos 66:106–120. https://doi.org/10.1016/j.cageo.2014.01.007 Dorafshan S, Thomas RJ, Maguire M (2018) Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete. Constr Build Mater 186:1031–1045. https://doi.org/10.1016/j.conbuildmat.2018.08.011 Abdel-Qader I, Abudayyeh O, Kelly ME (2003) Analysis of Edge-Detection Techniques for Crack Identification in Bridges. J Compu Civ Eng 17(4):255–263. https://doi.org/10.1061/(ASCE)0887-3801(2003)17:4(255) Hutchinson TC, Chen Z (2006) Improved Image Analysis for Evaluating Concrete Damage. J Compu Civ Eng 20:210–216. https://doi.org/10.1061/(ASCE)0887-3801(2006)20:3(210) Zhao, H, Qin, G, Wang X (2010) Improvement of canny algorithm based on pavement edge detection. 2010 3rd International Congress on Image and Signal Processing. IEEE. https://doi.org/10.1109/CISP.2010.5646923 Liu Y, Cho S, Jr B F S et al (2014) Automated assessment of cracks on concrete surfaces using adaptive digital image processing[J]. Smart Struct Syst 14(4):719–741. https://doi.org/10.12989/sss.2014.14.4.719 Liu Y-F, Cho S, Spencer BF, Fan J-S (2016) Concrete Crack Assessment Using Digital Image Processing and 3D Scene Reconstruction. J Compu Civ Eng 30(1):1–19. https://doi.org/10.1061/(asce)cp.1943-5487.0000446 Tanaka N, Uematsu K (1998) A crack detection method in road surface images using morphology[J]. IAPR MVA, Tokyo, 154–157. https://doi.org/10.2208/jsceje.62.631 Sinha SK, Fieguth PW (2006) Segmentation of buried concrete pipe images. Auto Constr 15(1):47–57. https://doi.org/10.1016/j.autcon.2005.02.007 Giakoumis I, Nikolaidis N, Pitas I (2006) Digital image processing techniques for the detection and removal of cracks in digitized paintings. IEEE Trans Image Process 15(1):178–188. https://doi.org/10.1109/tip.2005.860311 Aboutabit N (2020) Reduced Featured Based Projective Integral for Road Cracks Detection and Classification. Pattern Recognit Image Anal 30(2):247–255. https://doi.org/10.1134/s1054661820020029 Mei Q, Gül M (2020) Multi-level feature fusion in densely connected deep-learning architecture and depth-first search for crack segmentation on images collected with smartphones. Struct Health Monit 19(6):1726–1744. https://doi.org/10.1177/1475921719896813 Andrushia ADNA, Lubloy E (2021) Deep learning based thermal crack detection on structural concrete exposed to elevated temperature. Adv Struct Eng 24(9):1896–1909. https://doi.org/10.1177/1369433220986637 Hsieh Y-A, Tsai YJ (2020) Machine learning for crack detection: review and model performance comparison. J Comput Civ Eng 34:04020038. https://doi.org/10.1061/(ASCE) Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks[J]. Adv Neural Inf Process Syst 25(2). https://doi.org/10.1145/3065386 Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition[J]. Comput Sci. https://doi.org/10.48550/arXiv.1409.1556 Szegedy C, Liu W, Jia Y et al (2015) Going deeper with convolutions[J]. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2015.7298594 He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2016.90 Cha Y-J, Choi W, Büyüköztürk O (2017) Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks. Comput-Aided Civ Infrastruct Eng 32(5):361–378. https://doi.org/10.1111/mice.12263 Ni F, Zhang J, Chen Z (2018) Zernike-moment measurement of thin-crack width in images enabled by dual-scale deep learning. Comput-Aided Civ Infrastruct Eng 34(5):367–384. https://doi.org/10.1111/mice.12421 Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031 Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2016.91 Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: single shot multibox detector. Computer Vision – ECCV 2016. https://doi.org/10.1007/978-3-319-46448-0_2 Tabata AN, Zimmer A, dos Santos Coelho L, Mariani VC (2023) Analyzing CARLA ’s performance for 2D object detection and monocular depth estimation based on deep learning approaches. Expert Syst Appl 227:120. https://doi.org/10.1016/j.eswa.2023.120200 Ouali I, Ben Halima M, Wali A (2022) Real-time application for recognition and visualization of arabic words with vowels based DL and AR. 2022 International Wireless Communications and Mobile Computing (IWCMC), pp 678–683. https://doi.org/10.1109/IWCMC55113.2022.9825089 Che L, He Z, Zheng K, Si T, Ge M, Cheng H, Zeng L (2023) Deep learning in alloy material microstructures: Application and prospects. Mater Today Commun 37:107531. https://doi.org/10.1016/j.mtcomm.2023.107531 Yang X, Li H, Yu Y, Luo X, Huang T, Yang X (2018) Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network. Comput-Aided Civ Infrastruct Eng 33(12):1090–1109. https://doi.org/10.1111/mice.12412 Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pp 234–241. https://doi.org/10.1007/978-3-319-24574-4_28 Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615 Chen L-C, Zhu Y, Papandreou G, Schro F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. Computer Vision – ECCV 2018, pp 833–851. https://doi.org/10.1007/978-3-030-01234-2_49 Fu J, Liu J, Jiang J, Li Y, Bao Y, Lu H (2021) Scene Segmentation With Dual Relation-Aware Attention Network. IEEE Trans Neural Netw Learn Syst 32(6):2547–2560. https://doi.org/10.1109/TNNLS.2020.3006524 Cui X, Wang Q, Dai J, Li S, Xie C, Wang J (2022) Pixel-level intelligent recognition of concrete cracks based on DRACNN. Mater Lett 306:130867. https://doi.org/10.1016/j.matlet.2021.130867 Zhang T, Wang D, Mullins A, Lu Y (2023) Integrated APC-GAN and AttuNet Framework for Automated Pavement Crack Pixel-Level Segmentation: A New Solution to Small Training Datasets. IEEE trans Intell Transp Syst 24(4):4474–4481. https://doi.org/10.1109/tits.2023.3236247 Hong Z, Yang F, Pan H, Zhou R, Zhang Y, Han Y, Wang J, Yang S, Chen P, Tong X, Liu J (2022) Highway Crack Segmentation From Unmanned Aerial Vehicle Images Using Deep Learning. IEEE Geosci Remote Sens Lett 19:1–5. https://doi.org/10.1109/lgrs.2021.3129607 Su H, Wang X, Han T, Wang Z, Zhao Z, Zhang P (2022) Research on a U-Net Bridge Crack Identification and Feature-Calculation Methods Based on a CBAM Attention Mechanism. Build 12(10):1561. https://doi.org/10.3390/buildings12101561 Ren Y, Huang J, Hong Z, Lu W, Yin J, Zou L, Shen X (2020) Image-based concrete crack detection in tunnels using deep fully convolutional networks. Constr Build Mater 234:117367. https://doi.org/10.1016/j.conbuildmat.2019.117367 Qiao W, Zhang H, Zhu F, Wu Q, Lonetti P (2021) A Crack Identification Method for Concrete Structures Using Improved U-Net Convolutional Neural Networks. Math Probl Eng 2021:1–16. https://doi.org/10.1155/2021/6654996 Wang A, Togo R, Ogawa T, Haseyama M (2022) Defect Detection of Subway Tunnels Using Advanced U-Net Network. Sensors (Basel) 22(6):2330. https://doi.org/10.3390/s22062330 Cheng H, Li Y, Li H, Hu Q (2023) Embankment crack detection in UAV images based on efficient channel attention U2Net. Structures 50:430–443. https://doi.org/10.1016/j.istruc.2023.02.010 Shang J, Xu J, Zhang AA, Liu Y, Wang KCP, Ren D, Zhang H, Dong Z, He A (2023) Automatic Pixel-level pavement sealed crack detection using Multi-fusion U-Net network. Measurement 208:112475 Zhong T, Wang W, Lu S, Dong X, Yang B (2023) RMCHN: A Residual Modular Cascaded Heterogeneous Network for Noise Suppression in DAS-VSP Records. IEEE Geosci Remote Sens Lett 20:1–5. https://doi.org/10.1109/lgrs.2022.3229556 Woo S, Park J, Lee J-Y, Kweon IS (2018) CBAM: Convolutional Block Attention Module. Proceedings of the European conference on computer vision. Computer Vision – ECCV 2018, pp 3–19. https://doi.org/10.1007/978-3-030-01234-2_1 Choi W, Cha Y-J (2020) SDDNet: Real-Time Crack Segmentation. IEEE Trans Ind Electron 67(9):8016–8025. https://doi.org/10.1109/tie.2019.2945265 Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018) DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848. https://doi.org/10.1109/TPAMI.2017.2699184 Li P, Xia H, Zhou B, Yan F, Guo R (2022) A Method to Improve the Accuracy of Pavement Crack Identification by Combining a Semantic Segmentation and Edge Detection Model. Appl Sci 12(9):4714. https://doi.org/10.3390/app12094714