{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T23:49:20Z","timestamp":1784764160413,"version":"3.55.0"},"reference-count":60,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T00:00:00Z","timestamp":1579478400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["88881.311850\/2018-01"],"award-info":[{"award-number":["88881.311850\/2018-01"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["313887\/2018-7"],"award-info":[{"award-number":["313887\/2018-7"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005667","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa e Inova\u00e7\u00e3o do Estado de Santa Catarina","doi-asserted-by":"publisher","award":["2017TR1762"],"award-info":[{"award-number":["2017TR1762"]}],"id":[{"id":"10.13039\/501100005667","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study proposes and evaluates five deep fully convolutional networks (FCNs) for the semantic segmentation of a single tree species: SegNet, U-Net, FC-DenseNet, and two DeepLabv3+ variants. The performance of the FCN designs is evaluated experimentally in terms of classification accuracy and computational load. We also verify the benefits of fully connected conditional random fields (CRFs) as a post-processing step to improve the segmentation maps. The analysis is conducted on a set of images captured by an RGB camera aboard a UAV flying over an urban area. The dataset also contains a mask that indicates the occurrence of an endangered species called Dipteryx alata Vogel, also known as cumbaru, taken as the species to be identified. The experimental analysis shows the effectiveness of each design and reports average overall accuracy ranging from 88.9% to 96.7%, an F1-score between 87.0% and 96.1%, and IoU from 77.1% to 92.5%. We also realize that CRF consistently improves the performance, but at a high computational cost.<\/jats:p>","DOI":"10.3390\/s20020563","type":"journal-article","created":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T03:04:43Z","timestamp":1579575883000},"page":"563","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":105,"title":["Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7916-9463","authenticated-orcid":false,"given":"Daliana","family":"Lobo Torres","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8344-5096","authenticated-orcid":false,"given":"Raul","family":"Queiroz Feitosa","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3280-5471","authenticated-orcid":false,"given":"Patrick","family":"Nigri Happ","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6284-9494","authenticated-orcid":false,"given":"Laura","family":"Elena Cu\u00e9 La Rosa","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9096-6866","authenticated-orcid":false,"given":"Jos\u00e9","family":"Marcato Junior","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0668-8224","authenticated-orcid":false,"given":"Jos\u00e9","family":"Martins","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrik","family":"Ol\u00e3 Bressan","sequence":"additional","affiliation":[{"name":"Federal Institute of Mato Grosso do Sul, Jardim 79240-000, Brazil"},{"name":"Faculty of Computer Science, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8815-6653","authenticated-orcid":false,"given":"Wesley Nunes","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil"},{"name":"Faculty of Computer Science, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0564-7818","authenticated-orcid":false,"given":"Veraldo","family":"Liesenberg","sequence":"additional","affiliation":[{"name":"Department of Forest Engineering, Santa Catarina State University, Lages 88520-000, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.rse.2014.03.018","article-title":"Urban tree species mapping using hyperspectral and LiDAR data fusion","volume":"148","author":"Alonzo","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2016.03.014","article-title":"A survey on object detection in optical remote sensing images","volume":"117","author":"Cheng","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2016.08.013","article-title":"Review of studies on tree species classification from remotely sensed data","volume":"186","author":"Fassnacht","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3035","DOI":"10.1080\/01431169208904100","article-title":"A comparison of SPOT and Landsat-TM data for use in conducting inventories of forest resources","volume":"13","author":"Brockhaus","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"793","DOI":"10.14358\/PERS.69.7.793","article-title":"Land-Cover Change Monitoring with Classification Trees Using Landsat TM and Ancillary Data","volume":"69","author":"Rogan","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.rse.2005.04.013","article-title":"Classification of Amazonian Primary Rain Forest Vegetation using Landsat ETM+Satellite Imagery","volume":"97","author":"Salovaara","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"71","DOI":"10.14358\/PERS.72.1.71","article-title":"Mapping Structural Parameters and Species Composition of Riparian Vegetation Using IKONOS and Landsat ETM+ Data in Australian Tropical Savannahs","volume":"72","author":"Johansen","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.rse.2005.03.009","article-title":"Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales","volume":"96","author":"Clark","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhen, Z., Quackenbush, L.J., and Zhang, L. (2016). Trends in Automatic Individual Tree Crown Detection and Delineation\u2014Evolution of LiDAR Data. Remote Sens., 8.","DOI":"10.3390\/rs8040333"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, K., Wang, T., and Liu, X. (2018). A Review: Individual Tree Species Classification Using Integrated Airborne LiDAR and Optical Imagery with a Focus on the Urban Environment. Forests, 10.","DOI":"10.3390\/f10010001"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.apgeog.2008.10.001","article-title":"Modeling urban leaf area index with AISA+ hyperspectral data","volume":"29","author":"Jensen","year":"2009","journal-title":"Appl. Geogr."},{"key":"ref_12","first-page":"597","article-title":"An Assessment of Some Factors Influencing Multispectral Land-Cover Classification","volume":"56","author":"Gong","year":"1990","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chenari, A., Erfanifard, Y., Dehghani, M., and Pourghasemi, H.R. (2017, January 7\u201310). Woodland Mapping at Single-Tree Levels Using Object-Oriented Classification of Unmanned Aerial Vehicle (UAV) Images. Proceedings of the 2017 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Tehran, Iran.","DOI":"10.5194\/isprs-archives-XLII-4-W4-43-2017"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11119-012-9274-5","article-title":"The application of small unmanned aerial systems for precision agriculture: A review","volume":"13","author":"Zhang","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5006","DOI":"10.3390\/rs5105006","article-title":"Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture","volume":"5","author":"Honkavaara","year":"2013","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1890\/120150","article-title":"Lightweight unmanned aerial vehicles will revolutionize spatial ecology","volume":"11","author":"Anderson","year":"2013","journal-title":"Front. Ecol. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1177\/194008291200500202","article-title":"Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation","volume":"5","author":"Koh","year":"2012","journal-title":"Trop. Conserv. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"11051","DOI":"10.3390\/rs61111051","article-title":"UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas","volume":"6","author":"Barrado","year":"2014","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Feng, X., and Li, P. (2019). A Tree Species Mapping Method from UAV Images over Urban Area Using Similarity in Tree-Crown Object Histograms. Remote Sens., 11.","DOI":"10.3390\/rs11171982"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Baena, S., Moat, J., Whaley, O.Q., and Boyd, D.S. (2017). Identifying species from the air: UAVs and the very high resolution challenge for plant conservation. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0188714"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Santos, A.A.D., Marcato Junior, J., Ara\u00fajo, M.S., Di Martini, D.R., Tetila, E.C., Siqueira, H.L., Aoki, C., Eltner, A., Matsubara, E.T., and Pistori, H. (2019). Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs. Sensors, 19.","DOI":"10.3390\/s19163595"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mottaghi, R., Chen, X., Liu, X., Cho, N.G., Lee, S.W., Fidler, S., Urtasun, R., and Yuille, A. (2014, January 24\u201327). The Role of Context for Object Detection and Semantic Segmentation in the Wild. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.119"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., and Yuille, A.L. (2014). Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts. arXiv.","DOI":"10.1109\/CVPR.2014.254"},{"key":"ref_24","unstructured":"Badrinarayanan, V., Handa, A., and Cipolla, R. (2015). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e1264","DOI":"10.1002\/widm.1264","article-title":"Deep learning for remote sensing image classification: A survey","volume":"8","author":"Li","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, W., Fu, H., Yu, L., and Cracknell, A. (2017). Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images. Remote Sens., 9.","DOI":"10.3390\/rs9010022"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Weinstein, B.G., Marconi, S., Bohlman, S., Zare, A., and White, E. (2019). Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks. Remote Sens., 11.","DOI":"10.1101\/532952"},{"key":"ref_29","unstructured":"Natesan, S., Armenakis, C., and Vepakomma, U. (2019, January 10\u201314). Resnet-based tree species classification using uav images. Proceedings of the 2019 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Enschede, The Netherlands."},{"key":"ref_30","unstructured":"Onishi, M., and Ise, T. (2018). Automatic classification of trees using a UAV onboard camera and deep learning. arXiv."},{"key":"ref_31","first-page":"12","article-title":"Multiresolution segmentation: an optimization approach for high quality multi scale image segmentation","volume":"XII","author":"Baatz","year":"2000","journal-title":"Angew. Geogr. Informationsverarbeitung"},{"key":"ref_32","unstructured":"Shanmugamani, R. (2018). Deep Learning for Computer Vision: Expert Techniques to Train Advanced Neural Networks Using TensorFlow and Keras, Packt Publishing Ltd."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2014). Fully Convolutional Networks for Semantic Segmentation. arXiv.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_35","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 and Pattern Recognition, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, Y., Piramanayagam, S., Monteiro, S.T., and Saber, E. (2017, January 22\u201329). Dense semantic labeling of very-high-resolution aerial imagery and lidar with fully-convolutional neural networks and higher-order CRFs. Proceedings of the 2017 IEEE International Conference on Computer Vision and Pattern Recognition, Venice, Italy.","DOI":"10.1109\/CVPRW.2017.200"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2017.12.007","article-title":"Semantic labeling in very high resolution images via a self-cascaded convolutional neural network","volume":"145","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","unstructured":"Chen, L., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1002\/rse2.111","article-title":"Using the U-net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images","volume":"5","author":"Wagner","year":"2019","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-53797-9","article-title":"Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery","volume":"9","author":"Kattenborn","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_41","first-page":"31","article-title":"O baru (Dipteryx alata Vog.) como alternativa de sustentabilidade em \u00e1rea de fragmento florestal do Cerrado, no Mato Grosso do Sul","volume":"10","author":"Arakaki","year":"2009","journal-title":"Intera\u03c2\u00f5es (Campo Grande)"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, Y., Nguyen, D., Deligiannis, N., Ding, W., and Munteanu, A. (2017). Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9060522"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Volpi, M., and Tuia, D. (2016). Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks. arXiv.","DOI":"10.1109\/TGRS.2016.2616585"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., and Rodr\u00edguez, J.G. (2017). A Review on Deep Learning Techniques Applied to Semantic Segmentation. arXiv.","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"J\u00e9gou, S., Drozdzal, M., V\u00e1zquez, D., Romero, A., and Bengio, Y. (2017, January 22\u201329). The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Venice, Italy.","DOI":"10.1109\/CVPRW.2017.156"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Weinberger, K.Q. (2016). Densely Connected Convolutional Networks. arXiv.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1155\/2019\/8415485","article-title":"Fully Convolutional DenseNet with Multiscale Context for Automated Breast Tumor Segmentation","volume":"2019","author":"Hai","year":"2019","journal-title":"J. Health. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. arXiv.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Guo, Y., Li, Y., Feris, R.S., Wang, L., and Rosing, T. (2019). Depthwise Convolution is All You Need for Learning Multiple Visual Domains. arXiv.","DOI":"10.1609\/aaai.v33i01.33018368"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2016). Xception: Deep Learning with Depthwise Separable Convolutions. arXiv.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hariharan, B., Arbel\u00e1ez, P.A., Girshick, R.B., and Malik, J. (2014). Hypercolumns for Object Segmentation and Fine-grained Localization. arXiv.","DOI":"10.1109\/CVPR.2015.7298642"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"23173","DOI":"10.1364\/OE.27.023173","article-title":"Rapid and robust two-dimensional phase unwrapping via deep learning","volume":"27","author":"Zhang","year":"2019","journal-title":"Opt. Express"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., and Chen, L. (2018). Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation. arXiv.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Liu, Y., Ren, Q., Geng, J., Ding, M., and Li, J. (2018). Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images. Sensors, 18.","DOI":"10.3390\/s18103232"},{"key":"ref_55","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. arXiv."},{"key":"ref_56","unstructured":"Kr\u00e4henb\u00fchl, P., and Koltun, V. (2011). Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials. Proceedings of the 24th International Conference on Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_57","unstructured":"Chollet, F. (2020, January 20). Keras. Available online: https:\/\/keras.io."},{"key":"ref_58","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_59","first-page":"1015","article-title":"Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation","volume":"Volume 4304","author":"Sokolova","year":"2006","journal-title":"AI 2006: Advances in Artificial Intelligence"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Rezatofighi, S.H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I.D., and Savarese, S. (2019). Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression. arXiv.","DOI":"10.1109\/CVPR.2019.00075"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/2\/563\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:44:07Z","timestamp":1760363047000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/2\/563"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,20]]},"references-count":60,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20020563"],"URL":"https:\/\/doi.org\/10.3390\/s20020563","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,20]]}}}