{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T15:41:59Z","timestamp":1785426119398,"version":"3.56.0"},"reference-count":88,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T00:00:00Z","timestamp":1775952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["IH210100048"],"award-info":[{"award-number":["IH210100048"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DE220100094"],"award-info":[{"award-number":["DE220100094"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"award":["IH210100048"],"award-info":[{"award-number":["IH210100048"]}],"id":[{"id":"https:\/\/ror.org\/05mmh0f86","id-type":"ROR","asserted-by":"publisher"}]},{"award":["DE220100094"],"award-info":[{"award-number":["DE220100094"]}],"id":[{"id":"https:\/\/ror.org\/05mmh0f86","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>There have been significant advancements in object detection using extensive labelled datasets. However, existing learning-based approaches remain constrained in industrial environments, primarily due to the limited diversity in training datasets; the lack of generalisation of close-set detectors to unseen asset categories; and the inherent spatial and geometric complexity of mechanical, electrical, and plumbing (MEP) assets. To address this challenge, we propose a new approach that leverages pre-trained vision language models and close-set object detectors to detect unseen MEP assets using unlabelled data. Experimental results reveal the superior performance of Grounding DINO using Swin B transformer in open-vocabulary MEP asset detection, achieving the mean intersection over union (mIoU) of 0.6586 for valve detection and 0.4883 for pump detection. In addition, the combination of Grounding DINO (Swin B) and YOLOv8 outperforms other configurations in MEP asset detection, attaining the highest performance for both valve detection, with mean average precision at IoU = 0.5 (mAP50) of 0.928 and mean average precision over IoU threshold from 0.5 to 0.95 (mAP50:95) of 0.889, and pump detection, with corresponding values of 0.778 and 0.662, respectively. The quantitative and qualitative results of our approach were evaluated against fine-tuned Grounding DINO and fully supervised close-set object detectors.<\/jats:p>","DOI":"10.3390\/s26082379","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T08:49:02Z","timestamp":1776070142000},"page":"2379","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Vision Language-Based Framework for Detecting Industrial Mechanical, Electrical, and Plumbing Assets Using Unlabelled Data"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8226-3354","authenticated-orcid":false,"given":"Masoud","family":"Kamali","sequence":"first","affiliation":[{"name":"The Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9295-4220","authenticated-orcid":false,"given":"Behnam","family":"Atazadeh","sequence":"additional","affiliation":[{"name":"The Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4256-3173","authenticated-orcid":false,"given":"Abbas","family":"Rajabifard","sequence":"additional","affiliation":[{"name":"The Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0672-9217","authenticated-orcid":false,"given":"Yiqun","family":"Chen","sequence":"additional","affiliation":[{"name":"The Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ensiyeh","family":"Javaherian Pour","sequence":"additional","affiliation":[{"name":"The Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104735","DOI":"10.1016\/j.autcon.2022.104735","article-title":"As-built BIM reconstruction of piping systems using PipeNet","volume":"147","author":"Xie","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.autcon.2017.09.010","article-title":"Origins and probabilities of MEP and structural design clashes within a federated BIM model","volume":"85","author":"Edwards","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7292","DOI":"10.1080\/00207543.2020.1808257","article-title":"Exploring the impacts and contributions of maintenance function for sustainable manufacturing","volume":"58","author":"Holgado","year":"2020","journal-title":"Int. J. Prod. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102929","DOI":"10.1016\/j.aei.2024.102929","article-title":"Advancements in 3D digital model generation for digital twins in industrial environments: Knowledge gaps and future directions","volume":"62","author":"Kamali","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"104914","DOI":"10.1016\/j.autcon.2023.104914","article-title":"Recovering building information model from 2D drawings for mechanical, electrical and plumbing systems of ageing buildings","volume":"152","author":"Pan","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Rahimi, A., Anvaripour, M., and Hayat, K. (2021). Object detection using deep learning in a manufacturing plant to improve manual inspection. Proceedings of the 2021 IEEE International Conference on Prognostics and Health Management (ICPHM), Detroit (Romulus), MI, USA, IEEE.","DOI":"10.1109\/ICPHM51084.2021.9486529"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1109\/JBHI.2021.3094311","article-title":"Cervical cancer diagnostics healthcare system using hybrid object detection adversarial networks","volume":"26","author":"Elakkiya","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/TITS.2020.2972974","article-title":"Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges","volume":"22","author":"Feng","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"04018012","DOI":"10.1061\/(ASCE)CP.1943-5487.0000756","article-title":"Recognizing diverse construction activities in site images via relevance networks of construction-related objects detected by convolutional neural networks","volume":"32","author":"Luo","year":"2018","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"101009","DOI":"10.1016\/j.aei.2019.101009","article-title":"Convolutional neural networks for object detection in aerial imagery for disaster response and recovery","volume":"43","author":"Pi","year":"2020","journal-title":"Adv. Eng. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1561\/2000000071","article-title":"Deep learning in object recognition, detection, and segmentation","volume":"8","author":"Wang","year":"2016","journal-title":"Found. Trends Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Biffi, C., McDonagh, S., Torr, P., Leonardis, A., and Parisot, S. (2020). Many-Shot from Low-Shot: Learning to Annotate Using Mixed Supervision for Object Detection. European Conference on Computer Vision, Springer International Publishing.","DOI":"10.1007\/978-3-030-58598-3_3"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","article-title":"Deep learning for generic object detection: A survey","volume":"128","author":"Liu","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Abou Akar, C., Tekli, J., Jess, D., Khoury, M., Kamradt, M., and Guthe, M. (2022). Synthetic Object Recognition Dataset for Industries. Proceedings of the 2022 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), IEEE.","DOI":"10.1109\/SIBGRAPI55357.2022.9991784"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhou, X., Girdhar, R., Joulin, A., Kr\u00e4henb\u00fchl, P., and Misra, I. (2022, January 23\u201327). Detecting Twenty-thousand Classes using Image-level Supervision. Proceedings of the ECCV, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-20077-9_21"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8954","DOI":"10.1109\/TPAMI.2024.3413013","article-title":"A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future","volume":"46","author":"Zhu","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2979","DOI":"10.1007\/s11263-020-01355-6","article-title":"Zero-shot object detection: Joint recognition and localization of novel concepts","volume":"128","author":"Rahman","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rahman, S., Khan, S., and Porikli, F. (2018). Zero-shot object detection: Learning to simultaneously recognize and localize novel concepts. Proceedings of the Asian Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-20887-5_34"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhao, S., Gao, C., Shao, Y., Li, L., Yu, C., Ji, Z., and Sang, N. (2020, January 7\u201312). Gtnet: Generative transfer network for zero-shot object detection. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6996"},{"key":"ref_20","unstructured":"Liu, S., Zeng, Z., Ren, T., Li, F., Zhang, H., Yang, J., Li, C., Yang, J., Su, H., and Zhu, J. (October, January 29). Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection. Proceedings of the ECCV, Milan, Italy."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.-Y. (2023, January 2\u20136). Segment Anything. Proceedings of the ICCV, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Minderer, M., Gritsenko, A., Stone, A., Neumann, M., Weissenborn, D., Dosovitskiy, A., Mahendran, A., Arnab, A., Dehghani, M., and Shen, Z. (2022, January 23\u201327). Simple Open-Vocabulary Object Detection with Vision Transformers. Proceedings of the ECCV, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-20080-9_42"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Minderer, M., Gritsenko, A., and Houlsby, N. (2023, January 10\u201316). Scaling Open-Vocabulary Object Detection. Proceedings of the 37th International Conference on Neural Information Processing Systems, New Orleans, LA, USA.","DOI":"10.52202\/075280-3191"},{"key":"ref_24","unstructured":"Jocher, G., Ayush, C., Stoken, A., Borovec, J., Yonghye, K., Kalen, M., Jiacong, F. (ultralytics\/yolov5: V7.0-YOLOv5 SOTA Realtime Instance Segmentation, 2022). ultralytics\/yolov5: V7.0-YOLOv5 SOTA Realtime Instance Segmentation, Version v7.0."},{"key":"ref_25","unstructured":"Aharon, S., Louis, D., Ofri, M., Yurkova, K., Lotem, F., Khvedchenya, E., Rubin, R., Bagrov, N., and Tymchenko, B. (2021). Super-Gradients, Zenodo. GitHub repository."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-end object detection with transformers. Proceedings of the ECCV, Springer.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1786","DOI":"10.1016\/j.eng.2020.07.026","article-title":"Real-time detection of cracks on concrete bridge decks using deep learning in the frequency domain","volume":"7","author":"Zhang","year":"2021","journal-title":"Engineering"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105014","DOI":"10.1016\/j.autcon.2023.105014","article-title":"UAV-based road crack object-detection algorithm","volume":"154","author":"He","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"105085","DOI":"10.1016\/j.autcon.2023.105085","article-title":"Deep learning-based detection and condition classification of bridge steel bearings","volume":"156","author":"Wang","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"104555","DOI":"10.1016\/j.autcon.2022.104555","article-title":"Concrete dam damage detection and localisation based on YOLOv5s-HSC and photogrammetric 3D reconstruction","volume":"143","author":"Zhao","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105186","DOI":"10.1016\/j.autcon.2023.105186","article-title":"Surface defect detection of civil structures using images: Review from data perspective","volume":"158","author":"Guo","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104399","DOI":"10.1016\/j.autcon.2022.104399","article-title":"Detection and classification of pipe defects based on pipe-extended feature pyramid network","volume":"141","author":"Guo","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"105280","DOI":"10.1016\/j.autcon.2024.105280","article-title":"Intelligent detection of fastener defects in ballastless tracks based on deep learning","volume":"159","author":"Ye","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104744","DOI":"10.1016\/j.autcon.2023.104744","article-title":"Automated detection of dangerous work zone for crawler crane guided by UAV images via Swin Transformer","volume":"147","author":"Lu","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105103","DOI":"10.1016\/j.autcon.2023.105103","article-title":"Small object detection (SOD) system for comprehensive construction site safety monitoring","volume":"156","author":"Kim","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"104856","DOI":"10.1016\/j.autcon.2023.104856","article-title":"Small and overlapping worker detection at construction sites","volume":"151","author":"Park","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"105179","DOI":"10.1016\/j.autcon.2023.105179","article-title":"Automated detection of through-cracks in pavement using three-instantaneous attributes fusion and Swin Transformer network","volume":"158","author":"Huang","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104544","DOI":"10.1016\/j.autcon.2022.104544","article-title":"Pavement anomaly detection based on transformer and self-supervised learning","volume":"143","author":"Lin","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105260","DOI":"10.1016\/j.autcon.2023.105260","article-title":"Semi-supervised symbol detection for piping and instrumentation drawings","volume":"159","author":"Gupta","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1852","DOI":"10.1109\/TII.2020.2988208","article-title":"Fault description based attribute transfer for zero-sample industrial fault diagnosis","volume":"17","author":"Feng","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"101813","DOI":"10.1016\/j.aei.2022.101813","article-title":"Zero-shot surface defect recognition with class knowledge graph","volume":"54","author":"Li","year":"2022","journal-title":"Adv. Eng. Inform."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhao, S., Zhang, Z., Schulter, S., Zhao, L., Vijay Kumar, B., Stathopoulos, A., Chandraker, M., and Metaxas, D.N. (2022). Exploiting unlabeled data with vision and language models for object detection. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-031-20077-9_10"},{"key":"ref_43","unstructured":"Gu, X., Lin, T.-Y., Kuo, W., and Cui, Y. (2022, January 25\u201329). Open-vocabulary Object Detection via Vision and Language Knowledge Distillation. Proceedings of the ICLR, Virtual."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"105194","DOI":"10.1016\/j.autcon.2023.105194","article-title":"A transformer and self-cascade operation-based architecture for segmenting high-resolution bridge cracks","volume":"158","author":"Chu","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"105254","DOI":"10.1016\/j.autcon.2023.105254","article-title":"UAV-deployed deep learning network for real-time multi-class damage detection using model quantization techniques","volume":"159","author":"Yang","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"105083","DOI":"10.1016\/j.autcon.2023.105083","article-title":"Construction instance segmentation (CIS) dataset for deep learning-based computer vision","volume":"156","author":"Yan","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"104929","DOI":"10.1016\/j.autcon.2023.104929","article-title":"Crack detection and quantification for concrete structures using UAV and transformer","volume":"152","author":"Ding","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kufuor, J., Mohanty, D.D., Valero, E., and Bosch\u00e9, F. (2021). Automatic MEP component detection with deep learning. Proceedings of the International Conference on Pattern Recognition, Springer.","DOI":"10.1007\/978-3-030-68787-8_28"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wei, G., Zhou, D., and Yuan, X. (2025). Target Detection Technology of Mechanical, Electrical, and Plumbing Components Based on CV. Appl. Sci., 15.","DOI":"10.3390\/app15052803"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wang, D., and Fang, Y. (2025). Automatic BIM Reconstruction for Existing Building MEP Systems from Drawing Recognition. Buildings, 15.","DOI":"10.3390\/buildings15060924"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"104774","DOI":"10.1016\/j.autcon.2023.104774","article-title":"Digital twin development through auto-linking to manage legacy assets in nuclear power plants","volume":"148","author":"Edwards","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Guerra, E., Palacin, J., Wang, Z., and Grau, A. (2020). Deep learning-based detection of pipes in industrial environments. Industrial Robotics-New Paradigms, IntechOpen.","DOI":"10.5772\/intechopen.93164"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Guo, X., Su, X., Yuan, Y., Suo, T., and Liu, Y. (2021). A novel method for the complex tube system reconstruction and measurement. Sensors, 21.","DOI":"10.3390\/s21062207"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Bhanbhro, H., Hooi, Y.K., and Hassan, Z. (2022, January 1\u20132). Modern approaches towards object detection of complex engineering drawings. Proceedings of the 2022 International Conference on Digital Transformation and Intelligence (ICDI), Kuching, Malaysia.","DOI":"10.1109\/ICDI57181.2022.10007400"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"7294","DOI":"10.1080\/01431161.2023.2283900","article-title":"InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images","volume":"44","author":"Teichrieb","year":"2023","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Drost, B., Ulrich, M., Bergmann, P., Hartinger, P., and Steger, C. (2017, January 22\u201329). Introducing mvtec itodd-a dataset for 3d object recognition in industry. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.257"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"104499","DOI":"10.1016\/j.autcon.2022.104499","article-title":"SODA: A large-scale open site object detection dataset for deep learning in construction","volume":"142","author":"Duan","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_61","unstructured":"Oliver, A., Odena, A., Raffel, C.A., Cubuk, E.D., and Goodfellow, I. (2018, January 2\u20138). Realistic evaluation of deep semi-supervised learning algorithms. Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montr\u00e9al, QC, Canada."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.conbuildmat.2017.09.110","article-title":"Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection","volume":"157","author":"Gopalakrishnan","year":"2017","journal-title":"Constr. Build. Mater."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"105181","DOI":"10.1016\/j.autcon.2023.105181","article-title":"Efficient semi-supervised surface crack segmentation with small datasets based on consistency regularisation and pseudo-labelling","volume":"158","author":"Shamsabadi","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2251","DOI":"10.1109\/TPAMI.2018.2857768","article-title":"Zero-shot learning\u2014A comprehensive evaluation of the good, the bad and the ugly","volume":"41","author":"Xian","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"104773","DOI":"10.1016\/j.autcon.2023.104773","article-title":"Semi-supervised domain adaptation for segmentation models on different monitoring settings","volume":"149","author":"Hong","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"7535","DOI":"10.1109\/TII.2021.3053106","article-title":"Auxiliary information-guided industrial data augmentation for any-shot fault learning and diagnosis","volume":"17","author":"Zhuo","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Azeem, I., and Zaidi, M.A. (2021). Zero-Shot Learning-Based Detection of Electric Insulators in the Wild. Proceedings of the International Conference on Machine Learning, Optimization, and Data Science, Springer.","DOI":"10.1007\/978-3-030-95470-3_16"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Di Maggio, L.G., Brusa, E., and Delprete, C. (2023). Zero-Shot Generative AI for Rotating Machinery Fault Diagnosis: Synthesizing Highly Realistic Training Data via Cycle-Consistent Adversarial Networks. Appl. Sci., 13.","DOI":"10.3390\/app132212458"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Bravo, M.A., Mittal, S., and Brox, T. (2022). Localized vision-language matching for open-vocabulary object detection. Proceedings of the DAGM German Conference on Pattern Recognition, Springer.","DOI":"10.1007\/978-3-031-16788-1_24"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Tan, Z., Beigi, A., Wang, S., Guo, R., Bhattacharjee, A., Jiang, B., Karami, M., Li, J., Cheng, L., and Liu, H. (2024, January 12\u201316). Large Language Models for Data Annotation and Synthesis: A Survey. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Miami, FL, USA.","DOI":"10.18653\/v1\/2024.emnlp-main.54"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Xie, J., and Zheng, S. (2022). Zero-shot Object Detection Through Vision-Language Embedding Alignment. Proceedings of the 2022 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE.","DOI":"10.1109\/ICDMW58026.2022.00121"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Cheng, T., Song, L., Ge, Y., Liu, W., Wang, X., and Shan, Y. (2024). YOLO-World: Real-Time Open-Vocabulary Object Detection. arXiv.","DOI":"10.1109\/CVPR52733.2024.01599"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"105384","DOI":"10.1016\/j.autcon.2024.105384","article-title":"Omni-Scan2BIM: A ready-to-use Scan2BIM approach based on vision foundation models for MEP scenes","volume":"162","author":"Wang","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_74","unstructured":"Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., and El-Nouby, A. (2024). Dinov2: Learning robust visual features without supervision. arXiv."},{"key":"ref_75","unstructured":"Griwodz, C., Gasparini, S., Calvet, L., Gurdjos, P., Castan, F., Maujean, B., De Lillo, G., and Lanthony, Y. (October, January 28). AliceVision Meshroom: An open-source 3D reconstruction pipeline. Proceedings of the 12th ACM Multimedia Systems Conference, Istanbul, Turkey."},{"key":"ref_76","unstructured":"(2025, December 15). OpenAI, ChatGPT, Available online: https:\/\/chat.openai.com\/chat."},{"key":"ref_77","unstructured":"(2025, December 15). OpenAI, DALL.E 3, Available online: https:\/\/openai.com\/dall-e-3."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_79","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., and Clark, J. (2021, January 18\u201324). Learning transferable visual models from natural language supervision. Proceedings of the International Conference on Machine Learning, Virtual."},{"key":"ref_80","unstructured":"(2024, March 10). Roboflow Autodistill. Available online: https:\/\/github.com\/autodistill\/autodistill."},{"key":"ref_81","unstructured":"Sotoodeh, K. (2021). Safety and Reliability Improvements of Valves and Actuators for the Offshore Oil and Gas Industry Through Optimized Design. [Ph.D. Thesi, University of Stavange]."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"107581","DOI":"10.1016\/j.engfailanal.2023.107581","article-title":"A review of valve health diagnosis and assessment: Insights for intelligence maintenance of natural gas pipeline valves in China","volume":"153","author":"Zhang","year":"2023","journal-title":"Eng. Fail. Anal."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Werbinska-Wojciechowska, S., and Rogowski, R. (2025). Proactive Maintenance of Pump Systems Operating in the Mining Industry\u2014A Systematic Review. Sensors, 25.","DOI":"10.20944\/preprints202502.1128.v1"},{"key":"ref_84","unstructured":"(2025, July 01). NavVis. Available online: https:\/\/www.navvis.com\/resources\/content-library."},{"key":"ref_85","unstructured":"Gu, J., Han, Z., Chen, S., Beirami, A., He, B., Zhang, G., Liao, R., Qin, Y., Tresp, V., and Torr, P. (2023). A systematic survey of prompt engineering on vision-language foundation models. arXiv."},{"key":"ref_86","unstructured":"Efrat, A., and Levy, O. (2020). The turking test: Can language models understand instructions?. arXiv."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Rubin, O., Herzig, J., and Berant, J. (2022, January 10\u201315). Learning to retrieve prompts for in-context learning. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Seattle, WA, USA.","DOI":"10.18653\/v1\/2022.naacl-main.191"},{"key":"ref_88","unstructured":"Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q.V., and Zhou, D. (December, January 28). Chain-of-thought prompting elicits reasoning in large language models. Proceedings of the 36th International Conference on Neural Information Processing Systems, New Orleans, LA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/8\/2379\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T04:18:01Z","timestamp":1777004281000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/8\/2379"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,12]]},"references-count":88,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["s26082379"],"URL":"https:\/\/doi.org\/10.3390\/s26082379","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,12]]}}}