{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T21:39:34Z","timestamp":1774647574304,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100010667","name":"H2020 Industrial Leadership","doi-asserted-by":"publisher","award":["870133"],"award-info":[{"award-number":["870133"]}],"id":[{"id":"10.13039\/100010667","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010667","name":"H2020 Industrial Leadership","doi-asserted-by":"publisher","award":["870133"],"award-info":[{"award-number":["870133"]}],"id":[{"id":"10.13039\/100010667","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Tampere University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Semantic segmentation is one of the most important and studied problems in machine vision, which has been solved with high accuracy by many deep learning models. However, all these models present a significant drawback, they require large and diverse datasets to be trained. Gathering and annotating all these images manually would be extremely time-consuming, hence, numerous researchers have proposed approaches to facilitate or automate the process. Nevertheless, when the objects to be segmented are deformable, such as cables, the automation of this process becomes more challenging, as the dataset needs to represent their high diversity of shapes while keeping a high level of realism, and none of the existing solutions have been able to address it effectively. Therefore, this paper proposes a novel methodology to automatically generate highly realistic synthetic datasets of cables for training deep learning models in image segmentation tasks. This methodology utilizes Blender to create photo-realistic cable scenes and a Python pipeline to introduce random variations and natural deformations. To prove its performance, a dataset composed of 25000 synthetic cable images and their corresponding masks was generated and used to train six popular deep learning segmentation models. These models were then utilized to segment real cable images achieving outstanding results (over 70% IoU and 80% Dice coefficient for all the models). Both the methodology and the generated dataset are publicly available in the project\u2019s repository.<\/jats:p>","DOI":"10.1007\/s00138-024-01562-y","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T19:01:40Z","timestamp":1718910100000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Generation of realistic synthetic cable images to train deep learning segmentation models"],"prefix":"10.1007","volume":"35","author":[{"given":"Pablo","family":"MalvidoFresnillo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wael M.","family":"Mohammed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saigopal","family":"Vasudevan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jose A.","family":"PerezGarcia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jose L.","family":"MartinezLastra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,20]]},"reference":[{"issue":"7","key":"1562_CR1","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1177\/0278364918779698","volume":"37","author":"J Sanchez","year":"2018","unstructured":"Sanchez, J., Corrales, J.-A., Bouzgarrou, B.-C., Mezouar, Y.: Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey. Int. J. Robot. Res. 37(7), 688\u2013716 (2018). https:\/\/doi.org\/10.1177\/0278364918779698","journal-title":"Int. J. Robot. Res."},{"issue":"4","key":"1562_CR2","doi-asserted-by":"publisher","first-page":"2341","DOI":"10.1109\/TRO.2021.3139838","volume":"38","author":"N Lv","year":"2022","unstructured":"Lv, N., Liu, J., Jia, Y.: Dynamic modeling and control of deformable linear objects for single-arm and dual-arm robot manipulations. IEEE Trans. Rob. 38(4), 2341\u20132353 (2022). https:\/\/doi.org\/10.1109\/TRO.2021.3139838","journal-title":"IEEE Trans. Rob."},{"issue":"6","key":"1562_CR3","doi-asserted-by":"publisher","first-page":"2650","DOI":"10.1109\/TMECH.2018.2869477","volume":"23","author":"S Pirozzi","year":"2018","unstructured":"Pirozzi, S., Natale, C.: Tactile-based manipulation of wires for switchgear assembly. IEEE\/ASME Trans. Mechatron. 23(6), 2650\u20132661 (2018)","journal-title":"IEEE\/ASME Trans. Mechatron."},{"issue":"13","key":"1562_CR4","doi-asserted-by":"publisher","first-page":"4327","DOI":"10.3390\/s21134327","volume":"21","author":"P Kicki","year":"2021","unstructured":"Kicki, P., Bednarek, M., Lembicz, P., Mierzwiak, G., Szymko, A., Kraft, M., Walas, K.: Tell me, what do you see?-Interpretable classification of wiring harness branches with deep neural networks. Sensors 21(13), 4327 (2021). https:\/\/doi.org\/10.3390\/s21134327","journal-title":"Sensors"},{"issue":"4","key":"1562_CR5","doi-asserted-by":"publisher","first-page":"9075","DOI":"10.1109\/LRA.2022.3189791","volume":"7","author":"A Caporali","year":"2022","unstructured":"Caporali, A., Galassi, K., Zanella, R., Palli, G.: FASTDLO: fast deformable linear objects instance segmentation. IEEE Robot. Autom. Lett. 7(4), 9075\u20139082 (2022). https:\/\/doi.org\/10.1109\/LRA.2022.3189791","journal-title":"IEEE Robot. Autom. Lett."},{"issue":"2","key":"1562_CR6","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s00138-009-0199-6","volume":"22","author":"A Ortiz","year":"2011","unstructured":"Ortiz, A., Antich, J., Oliver, G.: A particle filter-based approach for tracking undersea narrow telecommunication cables. Mach. Vis. Appl. 22(2), 283\u2013302 (2011). https:\/\/doi.org\/10.1007\/s00138-009-0199-6","journal-title":"Mach. Vis. Appl."},{"key":"1562_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.mechatronics.2023.103085","volume":"96","author":"P Malvido Fresnillo","year":"2023","unstructured":"Malvido Fresnillo, P., Vasudevan, S., Mohammed, W.M., Martinez Lastra, J.L., Perez Garcia, J.A.: An approach based on machine vision for the identification and shape estimation of deformable linear objects. Mechatronics 96, 103085 (2023). https:\/\/doi.org\/10.1016\/j.mechatronics.2023.103085","journal-title":"Mechatronics"},{"issue":"9","key":"1562_CR8","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1016\/0031-3203(93)90135-J","volume":"26","author":"NR Pal","year":"1993","unstructured":"Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recogn. 26(9), 1277\u20131294 (1993). https:\/\/doi.org\/10.1016\/0031-3203(93)90135-J","journal-title":"Pattern Recogn."},{"issue":"7","key":"1562_CR9","doi-asserted-by":"publisher","first-page":"3523","DOI":"10.1109\/TPAMI.2021.3059968","volume":"44","author":"S Minaee","year":"2022","unstructured":"Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D.: Image segmentation using deep learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3523\u20133542 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3059968","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"1562_CR10","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamar\u00eda, J., Fadhel, M.A., Al-Amidie, M., Farhan, L.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8(1), 53 (2021). https:\/\/doi.org\/10.1186\/s40537-021-00444-8","journal-title":"J. Big Data"},{"key":"1562_CR11","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1562_CR12","doi-asserted-by":"publisher","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations (ICLR2015) (2015). https:\/\/doi.org\/10.48550\/arXiv.1409.1556 . arXiv:1409.1556","DOI":"10.48550\/arXiv.1409.1556"},{"key":"1562_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1562_CR14","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"3","key":"1562_CR15","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vision"},{"key":"1562_CR16","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015, pp. 234\u2013241 (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1562_CR17","doi-asserted-by":"publisher","unstructured":"Chaurasia, A., Culurciello, E.: LinkNet: Exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing (VCIP), pp. 1\u20134 (2017).https:\/\/doi.org\/10.1109\/VCIP.2017.8305148","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"1562_CR18","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"1562_CR19","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.660"},{"issue":"4","key":"1562_CR20","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2018). https:\/\/doi.org\/10.1109\/TPAMI.2017.2699184","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"1562_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-023-01500-4","volume":"35","author":"R Zhao","year":"2024","unstructured":"Zhao, R., Xie, M., Feng, X., Guo, M., Su, X., Zhang, P.: Interaction semantic segmentation network via progressive supervised learning. Mach. Vis. Appl. 35(2), 1\u201314 (2024). https:\/\/doi.org\/10.1007\/s00138-023-01500-4","journal-title":"Mach. Vis. Appl."},{"key":"1562_CR22","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"issue":"5","key":"1562_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-022-01308-8","volume":"33","author":"S Yarram","year":"2022","unstructured":"Yarram, S., Yuan, J., Yang, M.: Adversarial structured prediction for domain-adaptive semantic segmentation. Mach. Vis. Appl. 33(5), 1\u201313 (2022). https:\/\/doi.org\/10.1007\/s00138-022-01308-8","journal-title":"Mach. Vis. Appl."},{"issue":"4","key":"1562_CR24","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/s10032-022-00412-9","volume":"25","author":"A Dutta","year":"2022","unstructured":"Dutta, A., Biswas, S., Das, A.K.: BCBId: first Bangla comic dataset and its applications. Int. J. Doc. Anal. Recognit. (IJDAR) 25(4), 265\u2013279 (2022). https:\/\/doi.org\/10.1007\/s10032-022-00412-9","journal-title":"Int. J. Doc. Anal. Recognit. (IJDAR)"},{"issue":"1","key":"1562_CR25","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s11263-007-0090-8","volume":"77","author":"BC Russell","year":"2008","unstructured":"Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a database and web-based tool for image annotation. Int. J. Comput. Vision 77(1), 157\u2013173 (2008). https:\/\/doi.org\/10.1007\/s11263-007-0090-8","journal-title":"Int. J. Comput. Vision"},{"issue":"01","key":"1562_CR26","doi-asserted-by":"publisher","first-page":"5901","DOI":"10.1609\/aaai.v33i01.33015901","volume":"33","author":"H Zheng","year":"2019","unstructured":"Zheng, H., Yang, L., Chen, J., Han, J., Zhang, Y., Liang, P., Zhao, Z., Wang, C., Chen, D.Z.: Biomedical image segmentation via representative annotation. Proc. AAAI Conf. Artif. Intel. 33(01), 5901\u20135908 (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33015901","journal-title":"Proc. AAAI Conf. Artif. Intel."},{"key":"1562_CR27","doi-asserted-by":"crossref","unstructured":"Lin, D., Dai, J., Jia, J., He, K., Sun, J.: Scribblesup: scribble-supervised convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3159\u20133167 (2016)","DOI":"10.1109\/CVPR.2016.344"},{"key":"1562_CR28","doi-asserted-by":"crossref","unstructured":"Taylor, L., Nitschke, G.: Improving deep learning using generic data augmentation. arXiv (2017) arXiv:1708.06020","DOI":"10.1109\/SSCI.2018.8628742"},{"issue":"1","key":"1562_CR29","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 60 (2019). https:\/\/doi.org\/10.1186\/s40537-019-0197-0","journal-title":"J. Big Data"},{"key":"1562_CR30","doi-asserted-by":"crossref","unstructured":"Garai, A., Biswas, S., Mandal, S., Chaudhuri, B.B.: A method to generate synthetically warped document image. In: Computer Vision and Image Processing: 4th International Conference, CVIP 2019, Jaipur, India, September 27\u201329, 2019, Revised Selected Papers, Part I 4, pp. 270\u2013280 (2020). Springer","DOI":"10.1007\/978-981-15-4015-8_24"},{"key":"1562_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107621","volume":"109","author":"A Garai","year":"2021","unstructured":"Garai, A., Biswas, S., Mandal, S.: A theoretical justification of warping generation for Dewarping using CNN. Pattern Recogn. 109, 107621 (2021)","journal-title":"Pattern Recogn."},{"key":"1562_CR32","doi-asserted-by":"publisher","unstructured":"Zanella, R., Caporali, A., Tadaka, K., De\u00a0Gregorio, D., Palli, G.: Auto-generated wires dataset for semantic segmentation with domain-independence. In: 2021 International Conference on Computer, Control and Robotics (ICCCR), pp. 08\u201310. IEEE. https:\/\/doi.org\/10.1109\/ICCCR49711.2021.9349395","DOI":"10.1109\/ICCCR49711.2021.9349395"},{"key":"1562_CR33","doi-asserted-by":"publisher","unstructured":"Wahd, A.S., Kim, D., Lee, S.-I.: Cable instance segmentation with synthetic data generation. In: 2022 22nd international conference on control, automation and systems (ICCAS), pp. 1533\u20131538. IEEE. https:\/\/doi.org\/10.23919\/ICCAS55662.2022.10003680","DOI":"10.23919\/ICCAS55662.2022.10003680"},{"issue":"2","key":"1562_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-021-01171-z","volume":"32","author":"S Zhou","year":"2021","unstructured":"Zhou, S., Bi, Y., Wei, X., Liu, J., Ye, Z., Li, F., Du, Y.: Automated detection and classification of spilled loads on freeways based on improved YOLO network. Mach. Vis. Appl. 32(2), 1\u201312 (2021). https:\/\/doi.org\/10.1007\/s00138-021-01171-z","journal-title":"Mach. Vis. Appl."},{"key":"1562_CR35","doi-asserted-by":"publisher","unstructured":"Madaan, R., Maturana, D., Scherer, S.: Wire detection using synthetic data and dilated convolutional networks for unmanned aerial vehicles. In: 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 24\u201328. https:\/\/doi.org\/10.1109\/IROS.2017.8206190","DOI":"10.1109\/IROS.2017.8206190"},{"key":"1562_CR36","volume-title":"Physically Based Rendering, Second Edition: From Theory To Implementation","author":"M Pharr","year":"2010","unstructured":"Pharr, M., Humphreys, G.: Physically Based Rendering, Second Edition: From Theory To Implementation, 2nd edn. Morgan Kaufmann Publishers Inc., San Francisco (2010)","edition":"2"},{"key":"1562_CR37","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-1-4842-7954-0","volume-title":"Introduction to Blender 3.0: Learn Organic and Architectural Modeling, Lighting, Materials, Painting, Rendering, and Compositing with Blender","author":"G Moioli","year":"2022","unstructured":"Moioli, G.: Introduction to Blender 3.0: Learn Organic and Architectural Modeling, Lighting, Materials, Painting, Rendering, and Compositing with Blender, pp. 25\u201396. Apress, Berkeley (2022). https:\/\/doi.org\/10.1007\/978-1-4842-7954-0"},{"key":"1562_CR38","unstructured":"Denninger, M., Sundermeyer, M., Winkelbauer, D., Zidan, Y., Olefir, D., Elbadrawy, M., Lodhi, A., Katam, H.: BlenderProc. arXiv (2019) arXiv1911.01911"},{"key":"1562_CR39","doi-asserted-by":"publisher","unstructured":"Adam, R., Janciauskas, P., Ebel, T., Adam, J.: Synthetic training data generation and domain randomization for object detection in the formula student driverless framework. In: 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), pp. 16\u201318. IEEE. https:\/\/doi.org\/10.1109\/ICECCME55909.2022.9987772","DOI":"10.1109\/ICECCME55909.2022.9987772"},{"issue":"2","key":"1562_CR40","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1109\/LRA.2023.3234799","volume":"8","author":"A Caporali","year":"2023","unstructured":"Caporali, A., Pantano, M., Janisch, L., Regulin, D., Palli, G., Lee, D.: A weakly supervised semi-automatic image labeling approach for deformable linear objects. IEEE Rob. Autom. Lett. 8(2), 1013\u20131020 (2023). https:\/\/doi.org\/10.1109\/LRA.2023.3234799","journal-title":"IEEE Rob. Autom. Lett."},{"key":"1562_CR41","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/j.compag.2017.12.001","volume":"144","author":"R Barth","year":"2018","unstructured":"Barth, R., IJsselmuiden, J., Hemming, J., Henten, E.J.V.: Data synthesis methods for semantic segmentation in agriculture: a capsicum annuum dataset. Comput. Electron. Agric. 144, 284\u2013296 (2018). https:\/\/doi.org\/10.1016\/j.compag.2017.12.001","journal-title":"Comput. Electron. Agric."},{"key":"1562_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105378","volume":"173","author":"R Barth","year":"2020","unstructured":"Barth, R., Hemming, J., Van Henten, E.J.: Optimising realism of synthetic images using cycle generative adversarial networks for improved part segmentation. Comput. Electron. Agric. 173, 105378 (2020). https:\/\/doi.org\/10.1016\/j.compag.2020.105378","journal-title":"Comput. Electron. Agric."},{"key":"1562_CR43","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1007\/978-3-319-49409-8_75","volume-title":"Computer vision-ECCV 2016 workshops","author":"W Qiu","year":"2016","unstructured":"Qiu, W., Yuille, A.: UnrealCV: connecting computer vision to unreal engine. In: Computer vision-ECCV 2016 workshops, pp. 909\u2013916. Springer, Cham, Switzerland (2016)"},{"key":"1562_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105378","volume":"173","author":"R Barth","year":"2020","unstructured":"Barth, R., Hemming, J., Van Henten, E.J.: Optimising realism of synthetic images using cycle generative adversarial networks for improved part segmentation. Comput. Electron. Agric. 173, 105378 (2020). https:\/\/doi.org\/10.1016\/j.compag.2020.105378","journal-title":"Comput. Electron. Agric."},{"key":"1562_CR45","unstructured":"Textures. https:\/\/www.poliigon.com\/textures Accessed 2023-12-29"},{"key":"1562_CR46","unstructured":"HDRIs . Poly Haven. https:\/\/polyhaven.com\/hdris\/ Accessed 2023-12-29"},{"key":"1562_CR47","doi-asserted-by":"publisher","unstructured":"Fresnillo, P.M.: Realistic synthetic cable images and semantic segmentation masks dataset. https:\/\/doi.org\/10.23729\/93af7b3a-0f99-418b-9769-3ab8f345909a. Tampere University, Tekniikan ja luonnontieteiden tiedekunta","DOI":"10.23729\/93af7b3a-0f99-418b-9769-3ab8f345909a"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-024-01562-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-024-01562-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-024-01562-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T19:29:27Z","timestamp":1722454167000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-024-01562-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,20]]},"references-count":47,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["1562"],"URL":"https:\/\/doi.org\/10.1007\/s00138-024-01562-y","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,20]]},"assertion":[{"value":"15 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"84"}}