{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T19:10:45Z","timestamp":1776366645958,"version":"3.51.2"},"reference-count":81,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T00:00:00Z","timestamp":1684454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Federal Ministry of Education and Research","doi-asserted-by":"publisher","award":["13FH546IX6"],"award-info":[{"award-number":["13FH546IX6"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The monitoring of vital signs and increasing patient comfort are cornerstones of modern neonatal intensive care. Commonly used monitoring methods are based on skin contact which can cause irritations and discomfort in preterm neonates. Therefore, non-contact approaches are the subject of current research aiming to resolve this dichotomy. Robust neonatal face detection is essential for the reliable detection of heart rate, respiratory rate and body temperature. While solutions for adult face detection are established, the unique neonatal proportions require a tailored approach. Additionally, sufficient open-source data of neonates on the NICU is lacking. We set out to train neural networks with the thermal-RGB-fusion data of neonates. We propose a novel indirect fusion approach including the sensor fusion of a thermal and RGB camera based on a 3D time-of-flight (ToF) camera. Unlike other approaches, this method is tailored for close distances encountered in neonatal incubators. Two neural networks were used with the fusion data and compared to RGB and thermal networks. For the class \u201chead\u201d we reached average precision values of 0.9958 (RetinaNet) and 0.9455 (YOLOv3) for the fusion data. Compared with the literature, similar precision was achieved, but we are the first to train a neural network with fusion data of neonates. The advantage of this approach is in calculating the detection area directly from the fusion image for the RGB and thermal modality. This increases data efficiency by 66%. Our results will facilitate the future development of non-contact monitoring to further improve the standard of care for preterm neonates.<\/jats:p>","DOI":"10.3390\/s23104910","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T10:08:55Z","timestamp":1684490935000},"page":"4910","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Sensor Fusion for the Robust Detection of Facial Regions of Neonates Using Neural Networks"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2702-1077","authenticated-orcid":false,"given":"Johanna","family":"Gleichauf","sequence":"first","affiliation":[{"name":"Nuremberg Institute of Technology, 90489 Nuremberg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8198-7884","authenticated-orcid":false,"given":"Lukas","family":"Hennemann","sequence":"additional","affiliation":[{"name":"Nuremberg Institute of Technology, 90489 Nuremberg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5480-0204","authenticated-orcid":false,"given":"Fabian B.","family":"Fahlbusch","sequence":"additional","affiliation":[{"name":"Division of Neonatology and Pediatric Intensive Care, Department of Pediatrics and Adolescent Medicine, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"},{"name":"University Children\u2019s Hospital Augsburg, Neonatal and Pediatric Intensive Care Unit, 86156 Augsburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9714-9869","authenticated-orcid":false,"given":"Oliver","family":"Hofmann","sequence":"additional","affiliation":[{"name":"Nuremberg Institute of Technology, 90489 Nuremberg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5022-0544","authenticated-orcid":false,"given":"Christine","family":"Niebler","sequence":"additional","affiliation":[{"name":"Nuremberg Institute of Technology, 90489 Nuremberg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9071-5661","authenticated-orcid":false,"given":"Alexander","family":"Koelpin","sequence":"additional","affiliation":[{"name":"Hamburg University of Technology, 21073 Hamburg, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Larsen, R. (2016). An\u00e4sthesie und Intensivmedizin f\u00fcr die Fachpflege, Springer.","DOI":"10.1007\/978-3-662-50444-4"},{"key":"ref_2","unstructured":"Hausmann, J., Salekin, M.S., Zamzmi, G., Goldgof, D., and Sun, Y. (2022). Robust Neonatal Face Detection in Real-world Clinical Settings. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"St-Laurent, L., Pr\u00e9vost, D., and Maldague, X. (2010, January 24\u201329). Fast and accurate calibration-based thermal\/colour sensors registration. Proceedings of the 2010 International Conference on Quantitative InfraRed Thermography, Quebec, QC, Canada.","DOI":"10.21611\/qirt.2010.126"},{"key":"ref_4","unstructured":"Shivakumar, S.S., Rodrigues, N., Zhou, A., Miller, I.D., Kumar, V., and Taylor, C.J. (August, January 31). PST900: RGB-Thermal Calibration, Dataset and Segmentation Network. Proceedings of the IEEE International Conference on Robotics and Automation, Paris, France."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yang, M.D., Su, T.C., and Lin, H.Y. (2018). Fusion of infrared thermal image and visible image for 3D thermal model reconstruction using smartphone sensors. Sensors, 18.","DOI":"10.20944\/preprints201805.0225.v1"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1142\/S2301385017500054","article-title":"Cross-Calibration of RGB and Thermal Cameras with a LIDAR for RGB-Depth-Thermal Mapping","volume":"5","author":"Krishnan","year":"2017","journal-title":"Unmanned Syst."},{"key":"ref_7","unstructured":"Gusikhin, O., and Madani, K. (2020, January 7\u20139). Sensor Fusion Approach for an Autonomous Shunting Locomotive. Proceedings of the Informatics in Control, Automation and Robotics, Paris, France."},{"key":"ref_8","unstructured":"Tisha, S.M. (2019). LSU Digital Commons Thermal-Kinect Fusion Scanning System for Bodyshape Inpainting and Estimation under Clothing, Louisiana State University and Agricultural & Mechanical College."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, Q., Yang, R., Davis, J., and Nist\u00e9r, D. (2007, January 17\u201322). Spatial-depth super resolution for range images. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383211"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Van Baar, J., Beardsley, P., Pollefeys, M., and Gross, M. (2012, January 13\u201315). Sensor fusion for depth estimation, including TOF and thermal sensors. Proceedings of the 2nd Joint 3DIM\/3DPVT Conference: 3D Imaging, Modeling, Processing, Visualization and Transmission, 3DIMPVT 2012, Zurich, Switzerland.","DOI":"10.1109\/3DIMPVT.2012.69"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8179","DOI":"10.1364\/OE.26.008179","article-title":"Depth and thermal sensor fusion to enhance 3D thermographic reconstruction","volume":"26","author":"Cao","year":"2018","journal-title":"Opt. Express"},{"key":"ref_12","unstructured":"Pfitzner, C. (2018). Visual Human Body Weight Estimation with Focus on Medical Applications. [Ph.D. Thesis, Universit\u00e4t W\u00fcrzburg]."},{"key":"ref_13","first-page":"610","article-title":"RGB-D and Thermal Sensor Fusion - Application in Person Tracking","volume":"3","author":"Antunes","year":"2016","journal-title":"VISIGRAPP"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"24615","DOI":"10.3390\/s150924615","article-title":"A new approach for combining time-of-flight and RGB cameras based on depth-dependent planar projective transformations","volume":"15","author":"Salinas","year":"2015","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kim, Y.M., Theobalt, C., Diebel, J., Kosecka, J., Miscusik, B., and Thrun, S. (October, January 27). Multi-view image and ToF sensor fusion for dense 3D reconstruction. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009, Kyoto, Japan.","DOI":"10.1109\/ICCVW.2009.5457430"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.inffus.2022.03.007","article-title":"PIAFusion: A progressive infrared and visible image fusion network based on illumination aware","volume":"83\u201384","author":"Tang","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s43503-022-00002-y","article-title":"Fusion of thermal and RGB images for automated deep learning based crack detection in civil infrastructure","volume":"1","author":"Alexander","year":"2022","journal-title":"AI Civ. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"23912","DOI":"10.1109\/ACCESS.2020.2968559","article-title":"Fusionnet: Multispectral fusion of RGB and NIR images using two stage convolutional neural networks","volume":"8","author":"Jung","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1049\/ipr2.12431","article-title":"Infrared and visible image fusion based on multi-channel convolutional neural network","volume":"16","author":"Wang","year":"2022","journal-title":"IET Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, F., Wu, D., and Gao, G. (2022). Infrared and Visible Image Fusion Method Using Salience Detection and Convolutional Neural Network. Sensors, 22.","DOI":"10.3390\/s22145430"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, S., Luo, P., Loy, C.C., and Tang, X. (2015). WIDER FACE: A Face Detection Benchmark. arXiv.","DOI":"10.1109\/CVPR.2016.596"},{"key":"ref_22","unstructured":"Qi, D., Tan, W., Yao, Q., and Liu, J. (2021). YOLO5Face: Why Reinventing a Face Detector. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Zhou, Y., Yu, J., Kotsia, I., and Zafeiriou, S. (2019). RetinaFace: Single-stage Dense Face Localisation in the Wild. arXiv.","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","article-title":"Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks","volume":"23","author":"Kaipeng","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"237","DOI":"10.5194\/isprs-archives-XLII-2-W12-237-2019","article-title":"Detection of a human head on a low-quality image and its software implementation","volume":"XLII-2\/W12","author":"Yudin","year":"2019","journal-title":"ISPRS\u2014Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jiang, H., and Learned-Miller, E. (2016). Face Detection with the Faster R-CNN. arXiv.","DOI":"10.1109\/FG.2017.82"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cheong, Y.K., Yap, V.V., and Nisar, H. (2014, January 7\u20138). A novel face detection algorithm using thermal imaging. Proceedings of the 2014 IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE), Penang, Malaysia.","DOI":"10.1109\/ISCAIE.2014.7010239"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., and Scheunders, P. (2017, January 18\u201321). Face Detection in Thermal Infrared Images: A Comparison of Algorithm- and Machine-Learning-Based Approaches. Proceedings of the Advanced Concepts for Intelligent Vision Systems, Antwerp, Belgium.","DOI":"10.1007\/978-3-319-70353-4"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bebis, G., Boyle, R., Parvin, B., Koracin, D., Ushizima, D., Chai, S., Sueda, S., Lin, X., Lu, A., and Thalmann, D. (2019, January 7\u20139). Face Detection in Thermal Images with YOLOv3. Proceedings of the Advances in Visual Computing, Lake Tahoe, NV, USA.","DOI":"10.1007\/978-3-030-33720-9"},{"key":"ref_30","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Vukovi\u0107, T., Petrovi\u0107, R., Pavlovi\u0107, M., and Stankovi\u0107, S. (2019, January 26\u201327). Thermal Image Degradation Influence on R-CNN Face Detection Performance. Proceedings of the 2019 27th Telecommunications Forum (TELFOR), Belgrade, Serbia.","DOI":"10.1109\/TELFOR48224.2019.8971128"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Mucha, W., and Kampel, M. (2022, January 18\u201320). Depth and thermal images in face detection\u2014A detailed comparison between image modalities. Proceedings of the 2022 the 5th International Conference on Machine Vision and Applications (ICMVA), New York, NY, USA.","DOI":"10.1145\/3523111.3523114"},{"key":"ref_33","unstructured":"Jia, G., Jiankang, D., Alexandros, L., and Stefanos, Z. (2021). Sample and Computation Redistribution for Efficient Face Detection. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chaichulee, S., Villarroel, M., Jorge, J., Arteta, C., Green, G., McCormick, K., Zisserman, A., and Tarassenko, L. (June, January 30). Multi-Task Convolutional Neural Network for Patient Detection and Skin Segmentation in Continuous Non-Contact Vital Sign Monitoring. Proceedings of the 2017 12th IEEE International Conference on Automatic Face Gesture Recognition (FG 2017), Washington, DC, USA.","DOI":"10.1109\/FG.2017.41"},{"key":"ref_35","unstructured":"Cot\u00e9, G.L. (February, January 30). Localised photoplethysmography imaging for heart rate estimation of pre-term infants in the clinic. Proceedings of the Optical Diagnostics and Sensing XVIII: Toward Point-of-Care Diagnostics, San Francisco, CA, USA."},{"key":"ref_36","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. arXiv."},{"key":"ref_37","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017). Focal Loss for Dense Object Detection. arXiv.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kyrollos, D.G., Tanner, J.B., Greenwood, K., Harrold, J., and Green, J.R. (2021, January 23\u201325). Noncontact Neonatal Respiration Rate Estimation Using Machine Vision. Proceedings of the 2021 IEEE Sensors Applications Symposium (SAS), Sundsvall, Sweden.","DOI":"10.1109\/SAS51076.2021.9530013"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., and Bernstein, M. (2014). ImageNet Large Scale Visual Recognition Challenge. arXiv.","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lu, G., Wang, S., Kong, K., Yan, J., Li, H., and Li, X. (2018, January 28\u201330). Learning Pyramidal Hierarchical Features for Neonatal Face Detection. Proceedings of the 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Huangshan, China.","DOI":"10.1109\/FSKD.2018.8687197"},{"key":"ref_42","unstructured":"Jocher, G., Stoken, A., Borovec, J., NanoCode012, ChristopherSTAN, Changyu, L., Laughing, tkianai, Hogan, A., and lorenzomammana (2020). ultralytics\/yolov5: V3.1\u2014Bug Fixes and Performance Improvements, Zenodo."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Nagy, \u00c1., F\u00f6ldesy, P., J\u00e1noki, I., Terbe, D., Siket, M., Szab\u00f3, M., Varga, J., and Zar\u00e1ndy, \u00c1. (2021). Continuous camera-based premature-infant monitoring algorithms for NICU. Appl. Sci., 11.","DOI":"10.3390\/app11167215"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Khanam, F.T.Z., Perera, A.G., Al-Naji, A., Gibson, K., and Chahl, J. (2021). Non-contact automatic vital signs monitoring of infants in a Neonatal Intensive Care Unit based on neural networks. J. Imaging, 7.","DOI":"10.3390\/jimaging7080122"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"106796","DOI":"10.1016\/j.dib.2021.106796","article-title":"Multimodal neonatal procedural and postoperative pain assessment dataset","volume":"35","author":"Salekin","year":"2021","journal-title":"Data Brief"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"62893","DOI":"10.1109\/ACCESS.2022.3181167","article-title":"NICUface: Robust neonatal face detection in complex NICU scenes","volume":"10","author":"Dosso","year":"2022","journal-title":"IEEE Access"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3049","DOI":"10.1007\/s11517-020-02251-4","article-title":"Fast body part segmentation and tracking of neonatal video data using deep learning","volume":"58","author":"Antink","year":"2020","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s12938-023-01092-0","article-title":"Multi-modal body part segmentation of infants using deep learning","volume":"22","author":"Voss","year":"2023","journal-title":"Biomed. Eng. Online"},{"key":"ref_49","unstructured":"Beppu, F., Yoshikawa, H., Uchiyama, A., Higashino, T., Hamada, K., and Hirakawa, E. (2022). Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer International Publishing."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"59100","DOI":"10.1109\/ACCESS.2020.2982865","article-title":"Novel Framework: Face Feature Selection Algorithm for Neonatal Facial and Related Attributes Recognition","volume":"8","author":"Awais","year":"2020","journal-title":"IEEE Access"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/MCG.2008.49","article-title":"Color-Space CAD: Direct Gamut Editing in 3D","volume":"28","author":"Neophytou","year":"2008","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_52","unstructured":"Fairchild, M.D. (2013). Color Appearance Models, John Wiley & Sons. [3rd ed.]."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1016\/j.imavis.2006.01.017","article-title":"Face recognition by fusing thermal infrared and visible imagery","volume":"24","author":"Bebis","year":"2006","journal-title":"Image Vis. Comput."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Selinger, A., and Socolinsky, D.A. (2006). Appearance-Based Facial Recognition Using Visible and Thermal Imagery: A Comparative Study, EQUINOX Corp.","DOI":"10.21236\/ADA444419"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, H., Liang, Y., Meng, Y., and Wang, S. (2022). A novel infrared and visible image fusion approach based on adversarial neural network. Sensors, 22.","DOI":"10.3390\/s22010304"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Vadidar, M., Kariminezhad, A., Mayr, C., Kloeker, L., and Eckstein, L. (2022, January 4\u20139). Robust Environment Perception for Automated Driving: A Unified Learning Pipeline for Visual-Infrared Object Detection. Proceedings of the 2022 IEEE Intelligent Vehicles Symposium, Aachen, Germany.","DOI":"10.1109\/IV51971.2022.9827087"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Shopovska, I., Jovanov, L., and Philips, W. (2019). Deep visible and thermal image fusion for enhanced pedestrian visibility. Sensors, 19.","DOI":"10.3390\/s19173727"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhang, L., Zhuo, L., and Zhang, J. (2020). Object tracking in RGB-T videos using modal-aware attention network and competitive learning. Sensors, 20.","DOI":"10.3390\/s20020393"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"122122","DOI":"10.1109\/ACCESS.2019.2936914","article-title":"SiamFT: An RGB-Infrared Fusion Tracking Method via Fully Convolutional Siamese Networks","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_60","unstructured":"(2023, January 24). What Is a Visible Imaging Sensor (RGB Color Camera)?. Available online: https:\/\/www.infinitioptics.com\/glossary\/visible-imaging-sensor-400700nm-colour-cameras."},{"key":"ref_61","unstructured":"(2020, November 19). pmd FAQ. Available online: https:\/\/pmdtec.com\/picofamily\/faq\/."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Gleichauf, J., Herrmann, S., Hennemann, L., Krauss, H., Nitschke, J., Renner, P., Niebler, C., and Koelpin, A. (2021). Automated Non-Contact Respiratory Rate Monitoring of Neonates Based on Synchronous Evaluation of a 3D Time-of-Flight Camera and a Microwave Interferometric Radar Sensor. Sensors, 21.","DOI":"10.3390\/s21092959"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2016). Feature Pyramid Networks for Object Detection. arXiv.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast R-CNN. arXiv.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2016). YOLO9000: Better, Faster, Stronger. arXiv.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_67","unstructured":"Kathuria, A. (2023, March 02). Available online: https:\/\/towardsdatascience.com\/yolo-v3-object-detection-53fb7d3bfe6b."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Miko\u0142ajczyk, A., and Grochowski, M. (2018, January 9\u201312). Data augmentation for improving deep learning in image classification problem. Proceedings of the 2018 International Interdisciplinary PhD Workshop (IIPhDW), Swinoujscie, Poland.","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"ref_69","unstructured":"Hennemann, L. (2023). Realisierung und Optimierung der Detektion von K\u00f6rperregionen Neugeborener zur Kontaktlosen und Robusten \u00dcberwachung der Vitalparameter mittels eines Neuronalen Netzes. [Master\u2019s Thesis, Nuremberg Institute of Technology]."},{"key":"ref_70","unstructured":"May, S. (2023, January 24). optris_drivers. Available online: https:\/\/wiki.ros.org\/optris_drivers."},{"key":"ref_71","unstructured":"Hartmann, C., and Gleichauf, J. (2019, June 07). ros_cvb_camera_driver. Available online: http:\/\/wiki.ros.org\/ros_cvb_camera_driver."},{"key":"ref_72","unstructured":"For Artificial Intelligence University of Bremen, I. (2020, April 29). pico_flexx_driver. Available online: https:\/\/github.com\/code-iai\/pico_flexx_driver."},{"key":"ref_73","unstructured":"(2019, January 14). camera_calibration. Available online: https:\/\/wiki.ros.org\/camera_calibration."},{"key":"ref_74","unstructured":"Ocana, D.T. (2019, June 07). image_pipeline. Available online: https:\/\/github.com\/DavidTorresOcana\/image_pipeline."},{"key":"ref_75","unstructured":"openCV (2022, May 12). How to Detect Ellipse and Get Centers of Ellipse. Available online: https:\/\/answers.opencv.org\/question\/38885\/how-to-detect-ellipse-and-get-centers-of-ellipse\/."},{"key":"ref_76","unstructured":"(2022, May 12). opencv 3, Blobdetection, The Function\/Feature Is Not Implemented () in detectAndCompute. Available online: https:\/\/stackoverflow.com\/questions\/30622304\/opencv-3-blobdetection-the-function-feature-is-not-implemented-in-detectand."},{"key":"ref_77","unstructured":"openCV (2022, May 12). solvePnP. Available online: https:\/\/docs.opencv.org\/3.4\/d9\/d0c\/group__calib3d.htmlga549c2075fac14829ff4a58bc931c033d."},{"key":"ref_78","unstructured":"openCV (2022, May 12). Rodrigues. Available online: https:\/\/docs.opencv.org\/3.4\/d9\/d0c\/group__calib3d.htmlga61585db663d9da06b68e70cfbf6a1eac."},{"key":"ref_79","unstructured":"openCV (2022, May 12). projectPoints. Available online: https:\/\/docs.opencv.org\/3.4\/d9\/d0c\/group__calib3d.htmlga1019495a2c8d1743ed5cc23fa0daff8c."},{"key":"ref_80","unstructured":"Fizyr (2023, May 10). Keras-Retinanet. Available online: https:\/\/github.com\/fizyr\/keras-retinanet."},{"key":"ref_81","unstructured":"AlexeyAB (2023, May 10). Darknet. Available online: https:\/\/github.com\/AlexeyAB\/darknet."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4910\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:38:42Z","timestamp":1760125122000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4910"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,19]]},"references-count":81,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23104910"],"URL":"https:\/\/doi.org\/10.3390\/s23104910","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,19]]}}}