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Constr."},{"key":"ref_4","first-page":"3","article-title":"Defect detection and repair of urban underground drainage pipeline","volume":"17","author":"Zhu","year":"2017","journal-title":"J. Jiangsu Vocat. Inst. Archit. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.conbuildmat.2005.06.049","article-title":"Evaluation of building materials using infrared thermography","volume":"21","author":"Barreira","year":"2007","journal-title":"Constr. Build. Mater."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s00138-005-0012-0","article-title":"Applications, Morphological segmentation and classification of underground pipe images","volume":"17","author":"Sinha","year":"2006","journal-title":"Mach. Vis. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.autcon.2005.02.007","article-title":"Segmentation of buried concrete pipe images","volume":"15","author":"Sinha","year":"2006","journal-title":"Autom. Constr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1111\/j.1467-8667.2006.00445.x","article-title":"Segmentation of Pipe Images for Crack Detection in Buried Sewers","volume":"21","author":"Iyer","year":"2006","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_9","first-page":"2454","article-title":"Pipes information recognition and 3D model reconstruction based on DWGDirect","volume":"427\u2013429","author":"Li","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Huynh, P., Ross, R., Martchenko, A., and Devlin, J. (2016, January 5\u20137). 3D anomaly inspection system for sewer pipes using stereo vision and novel image processing. Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), Hefei, China.","DOI":"10.1109\/ICIEA.2016.7603726"},{"key":"ref_11","first-page":"9","article-title":"Image-Based Framework for Concrete Surface Crack Monitoring and Quantification","volume":"2010","author":"Chen","year":"2010","journal-title":"Adv. Civ. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.3390\/s16101646","article-title":"Analysis of Crack Image Recognition Characteristics in Concrete Structures Depending on the Illumination and Image Acquisition Distance through Outdoor Experiments","volume":"16","year":"2016","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1142\/S1793351X16500045","article-title":"Deep Learning","volume":"10","author":"Hao","year":"2016","journal-title":"Int. J. Semant. Comput."},{"key":"ref_14","first-page":"15","article-title":"Deep learning with big data: State of the art and development","volume":"11","author":"MA","year":"2016","journal-title":"CAAI Trans. Intell. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.aiopen.2021.05.002","article-title":"Robustness of deep learning models on graphs: A survey","volume":"2","author":"Xu","year":"2021","journal-title":"AI Open"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1111\/mice.12481","article-title":"A unified convolutional neural network integrated with conditional random field for pipe defect segmentation","volume":"35","author":"Wang","year":"2020","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103383","DOI":"10.1016\/j.autcon.2020.103383","article-title":"Automatic sewer pipe defect semantic segmentation based on improved U-Net","volume":"119","author":"Pan","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104403","DOI":"10.1016\/j.tust.2022.104403","article-title":"Automatic sewer defect detection and severity quantification based on pixel-level semantic segmentation","volume":"123","author":"Zhou","year":"2022","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"22166","DOI":"10.1109\/TITS.2022.3161960","article-title":"Automatic detection and counting system for pavement cracks based on PCGAN and YOLO-MF","volume":"23","author":"Ma","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"He, M., Zhao, Q., Gao, H., Zhang, X., and Zhao, Q. (2022). Image segmentation of a sewer based on deep learning. Sustainability, 14.","DOI":"10.3390\/su14116634"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"110727","DOI":"10.1016\/j.measurement.2022.110727","article-title":"A robust instance segmentation framework for underground sewer defect detection","volume":"190","author":"Li","year":"2022","journal-title":"Measurement"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.autcon.2018.08.005","article-title":"Automated detection of faults in sewers using CCTV image sequences","volume":"95","author":"Myrans","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_23","first-page":"852","article-title":"Alias-Free Generative Adversarial Networks","volume":"34","author":"Karras","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","first-page":"1137","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bolya, D., Zhou, C., Xiao, F., and Lee, Y.J. (2019, January 2). Yolact: Real-time instance segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00925"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, R., Tian, Z., Shen, C., You, M., and Yan, Y. (2020, January 14\u201319). Mask Encoding for Single Shot Instance Segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01024"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., and Chen, H. (2020, January 23\u201328). Conditional convolutions for instance segmentation. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part I 16.","DOI":"10.1007\/978-3-030-58452-8_17"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (2019, January 2). FCOS: Fully Convolutional One-Stage Object Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, X., Kong, T., Shen, C., Jiang, Y., and Li, L. (2020, January 23\u201328). SOLO: Segmenting Objects by Locations. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58523-5_38"},{"key":"ref_31","first-page":"17721","article-title":"SOLOv2: Dynamic and Fast Instance Segmentation","volume":"33","author":"Wang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cheng, T., Wang, X., Chen, S., Zhang, W., Zhang, Q., Huang, C., Zhang, Z., and Liu, W. (2022, January 14\u201318). Sparse instance activation for real-time instance segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00439"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23\u201328). End-to-end object detection with transformers. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Stewart, R., Andriluka, M., and Ng, A.Y. (2016, January 27\u201330). End-to-end people detection in crowded scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.255"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision, Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 23\u201328). Microsoft COCO: Common objects in context. 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