{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T16:11:41Z","timestamp":1783613501248,"version":"3.55.0"},"reference-count":19,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T00:00:00Z","timestamp":1675814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Science and Innovation projects","award":["PID2021-123390OB-C21"],"award-info":[{"award-number":["PID2021-123390OB-C21"]}]},{"name":"Spanish Science and Innovation projects","award":["RTI2018-096333-B-I00"],"award-info":[{"award-number":["RTI2018-096333-B-I00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>During the last few years, supervised deep convolutional neural networks have become the state-of-the-art for image recognition tasks. Nevertheless, their performance is severely linked to the amount and quality of the training data. Acquiring and labeling data is a major challenge that limits their expansion to new applications, especially with limited data. Recognition of Lego bricks is a clear example of a real-world deep learning application that has been limited by the difficulties associated with data gathering and training. In this work, photo-realistic image synthesis and few-shot fine-tuning are proposed to overcome limited data in the context of Lego bricks recognition. Using synthetic images and a limited set of 20 real-world images from a controlled environment, the proposed system is evaluated on controlled and uncontrolled real-world testing datasets. Results show the good performance of the synthetically generated data and how limited data from a controlled domain can be successfully used for the few-shot fine-tuning of the synthetic training without a perceptible narrowing of its domain. Obtained results reach an AP50 value of 91.33% for uncontrolled scenarios and 98.7% for controlled ones.<\/jats:p>","DOI":"10.3390\/s23041898","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T04:21:59Z","timestamp":1675830119000},"page":"1898","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Brickognize: Applying Photo-Realistic Image Synthesis for Lego Bricks Recognition with Limited Data"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8287-688X","authenticated-orcid":false,"given":"Joel","family":"Vidal","sequence":"first","affiliation":[{"name":"Computer Vision and Robotics Institute, University of Girona, 17003 Girona, Spain"},{"name":"Tramacsoft GmbH, Schlo\u00dfstra\u00dfe 52, 60486 Frankfurt am Main, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5942-1895","authenticated-orcid":false,"given":"Guillem","family":"Vallicrosa","sequence":"additional","affiliation":[{"name":"Tramacsoft GmbH, Schlo\u00dfstra\u00dfe 52, 60486 Frankfurt am Main, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8080-2710","authenticated-orcid":false,"given":"Robert","family":"Mart\u00ed","sequence":"additional","affiliation":[{"name":"Computer Vision and Robotics Institute, University of Girona, 17003 Girona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3666-9692","authenticated-orcid":false,"given":"Marc","family":"Barnada","sequence":"additional","affiliation":[{"name":"Tramacsoft GmbH, Schlo\u00dfstra\u00dfe 52, 60486 Frankfurt am Main, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103760","DOI":"10.1016\/j.autcon.2021.103760","article-title":"Classification and analysis of deep learning applications in construction: A systematic literature review","volume":"129","author":"Khallaf","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Silva, M.F., Lu\u00eds Lima, J., Reis, L.P., Sanfeliu, A., and Tardioli, D. (2020). Proceedings of the Robot 2019: Fourth Iberian Robotics Conference, Springer International Publishing.","DOI":"10.1007\/978-3-030-35990-4"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1002\/mp.13264","article-title":"Deep learning in medical imaging and radiation therapy","volume":"46","author":"Sahiner","year":"2019","journal-title":"Med. Phys."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., and Asari, V.K. (2019). A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics, 8.","DOI":"10.3390\/electronics8030292"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). Proceedings of the Computer Vision\u2014ECCV 2014, Springer International Publishing.","DOI":"10.1007\/978-3-319-10599-4"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The Pascal Visual Object Classes Challenge: A Retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_7","unstructured":"Brickset (2022, October 19). Browse Parts. Available online: https:\/\/brickset.com\/browse\/parts\/."},{"key":"ref_8","unstructured":"(2022, July 14). Instabrick. Available online: https:\/\/www.instabrick.org\/."},{"key":"ref_9","unstructured":"(2022, July 14). RebrickNet. Available online: https:\/\/rebrickable.com\/rebricknet\/."},{"key":"ref_10","unstructured":"(2022, July 14). Brickit. Available online: https:\/\/brickit.app\/."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_13","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_14","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_15","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 IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_16","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_17","first-page":"430","article-title":"Stochastic Gradient Descent Tricks","volume":"Volume 7700","author":"Bottou","year":"2012","journal-title":"Neural Networks, Tricks of the Trade, Reloaded"},{"key":"ref_18","unstructured":"Community, B.O. (2018). Blender\u2014A 3D Modelling and Rendering Package, Blender Foundation, Stichting Blender Foundation."},{"key":"ref_19","unstructured":"Denninger, M., Sundermeyer, M., Winkelbauer, D., Zidan, Y., Olefir, D., Elbadrawy, M., Lodhi, A., and Katam, H. (2019). BlenderProc. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1898\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:27:57Z","timestamp":1760120877000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1898"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,8]]},"references-count":19,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23041898"],"URL":"https:\/\/doi.org\/10.3390\/s23041898","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,8]]}}}