{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:03:15Z","timestamp":1783180995759,"version":"3.54.6"},"reference-count":48,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T00:00:00Z","timestamp":1655769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["1921060"],"award-info":[{"award-number":["1921060"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["N00014-20-1-2085"],"award-info":[{"award-number":["N00014-20-1-2085"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"ONR","doi-asserted-by":"publisher","award":["1921060"],"award-info":[{"award-number":["1921060"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"ONR","doi-asserted-by":"publisher","award":["N00014-20-1-2085"],"award-info":[{"award-number":["N00014-20-1-2085"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Obstacle detection for autonomous navigation through semantic image segmentation using neural networks has grown in popularity for use in unmanned ground and surface vehicles because of its ability to rapidly create a highly accurate pixel-wise classification of complex scenes. Due to the lack of available training data, semantic networks are rarely applied to navigation in complex water scenes such as rivers, creeks, canals, and harbors. This work seeks to address the issue by making a one-of-its-kind River Obstacle Segmentation En-Route By USV Dataset (ROSEBUD) publicly available for use in robotic SLAM applications that map water and non-water entities in fluvial images from the water level. ROSEBUD provides a challenging baseline for surface navigation in complex environments using complex fluvial scenes. The dataset contains 549 images encompassing various water qualities, seasons, and obstacle types that were taken on narrow inland rivers and then hand annotated for use in semantic network training. The difference between the ROSEBUD dataset and existing marine datasets was verified. Two state-of-the-art networks were trained on existing water segmentation datasets and tested for generalization to the ROSEBUD dataset. Results from further training show that modern semantic networks custom made for water recognition, and trained on marine images, can properly segment large areas, but they struggle to properly segment small obstacles in fluvial scenes without further training on the ROSEBUD dataset.<\/jats:p>","DOI":"10.3390\/s22134681","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T04:12:01Z","timestamp":1655871121000},"page":"4681","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["ROSEBUD: A Deep Fluvial Segmentation Dataset for Monocular Vision-Based River Navigation and Obstacle Avoidance"],"prefix":"10.3390","volume":"22","author":[{"given":"Reeve","family":"Lambert","sequence":"first","affiliation":[{"name":"The School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2032-2296","authenticated-orcid":false,"given":"Jalil","family":"Chavez-Galaviz","sequence":"additional","affiliation":[{"name":"The School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3519-6405","authenticated-orcid":false,"given":"Jianwen","family":"Li","sequence":"additional","affiliation":[{"name":"The School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nina","family":"Mahmoudian","sequence":"additional","affiliation":[{"name":"The School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1175\/JTECH-D-20-0010.1","article-title":"Evaluation of a New Carbon Dioxide System for Autonomous Surface Vehicles","volume":"37","author":"Sabine","year":"2020","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bertram, S., Kitts, C., Azevedo, D., Vecchio, G.D., Hopner, B., Wheat, G., and Kirkwood, W. (2016, January 19\u201323). A portable ASV prototype for shallow-water science operations. Proceedings of the OCEANS 2016 MTS\/IEEE Monterey, Monterey, CA, USA.","DOI":"10.1109\/OCEANS.2016.7761403"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ishigami, G., and Yoshida, K. (2021). Dynamic Autonomous Surface Vehicle Controls Under Changing Environmental Forces. Field and Service Robotics, Springer.","DOI":"10.1007\/978-981-15-9460-1"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s00027-015-0438-z","article-title":"Classification of river morphology and hydrology to support management and restoration","volume":"78","author":"Rinaldi","year":"2015","journal-title":"Aquat. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.advwatres.2015.11.009","article-title":"On the seasonality of flooding across the continental United States","volume":"87","author":"Villarini","year":"2016","journal-title":"Adv. Water Resour."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bovcon, B., Muhovic, J., Pers, J., and Kristan, M. (2019, January 3\u20138). The MaSTr1325 dataset for training deep USV obstacle detection models. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967909"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bovcon, B., and Kristan, M. (2021). WaSR\u2014A Water Segmentation and Refinement Maritime Obstacle Detection Network. IEEE Trans. Cybern.","DOI":"10.1109\/TCYB.2021.3085856"},{"key":"ref_8","first-page":"1","article-title":"WODIS: Water Obstacle Detection Network Based on Image Segmentation for Autonomous Surface Vehicles in Maritime Environments","volume":"70","author":"Chen","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103346","DOI":"10.1016\/j.robot.2019.103346","article-title":"Waterline and obstacle detection in images from low-cost autonomous boats for environmental monitoring","volume":"124","author":"Steccanella","year":"2020","journal-title":"Robot. Auton. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xingyun, Q., Shaobin, C., and Yanwei, H. (2019, January 22\u201324). An Algorithm for Identification of Inland River Shorelines based on Phase Correlation Algorithm. Proceedings of the 2019 Chinese Automation Congress (CAC), Hangzhou, China.","DOI":"10.1109\/CAC48633.2019.8996801"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1002\/rob.21989","article-title":"River segmentation for autonomous surface vehicle localization and river boundary mapping","volume":"38","author":"Meier","year":"2020","journal-title":"J. Field Robot."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yu, J., Lin, Y., Zhu, Y., Xu, W., Hou, D., Huang, P., and Zhang, G. (2020). Segmentation of River Scenes Based on Water Surface Reflection Mechanism. Appl. Sci., 10.","DOI":"10.3390\/app10072471"},{"key":"ref_13","unstructured":"Mettes, P., Tan, R.T., and Veltkamp, R. (2014, January 5\u20138). On the segmentation and classification of water in videos. Proceedings of the 2014 International Conference on Computer Vision Theory and Applications (VISAPP), Lisbon, Portugal."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Achar, S., Sankaran, B., Nuske, S., Scherer, S., and Singh, S. (2011, January 9\u201313). Self-supervised segmentation of river scenes. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980157"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/BF00133570","article-title":"Snakes: Active contour models","volume":"1","author":"Kass","year":"1988","journal-title":"Int. J. Comput. Vis."},{"key":"ref_16","unstructured":"Hahmann, T., and Wessel, B. (2010, January 7\u201310). Surface Water Body Detection in High-Resolution TerraSAR-X Data using Active Contour Models. Proceedings of the 8th European Conference on Synthetic Aperture Radar, Aachen, Germany."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"40347","DOI":"10.1109\/ACCESS.2019.2905847","article-title":"Factorization-Based Active Contour for Water-Land SAR Image Segmentation via the Fusion of Features","volume":"7","author":"Meng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.patrec.2022.04.025","article-title":"A hybrid active contour model based on pre-fitting energy and adaptive functions for fast image segmentation","volume":"158","author":"Ge","year":"2022","journal-title":"Pattern Recognit. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"115633","DOI":"10.1016\/j.eswa.2021.115633","article-title":"A level set method based on additive bias correction for image segmentation","volume":"185","author":"Weng","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lopez-Fuentes, L., Rossi, C., and Skinnemoen, H. (2017, January 11\u201314). River segmentation for flood monitoring. Proceedings of the 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA.","DOI":"10.1109\/BigData.2017.8258373"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cane, T., and Ferryman, J. (2018, January 27\u201330). Evaluating deep semantic segmentation networks for object detection in maritime surveillance. Proceedings of the 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Auckland, New Zealand.","DOI":"10.1109\/AVSS.2018.8639077"},{"key":"ref_22","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_23","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 (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/LRA.2018.2795643","article-title":"DroNet: Learning to Fly by Driving","volume":"3","author":"Loquercio","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yao, J., Ramalingam, S., Taguchi, Y., Miki, Y., and Urtasun, R. (2015, January 5\u20139). Estimating Drivable Collision-Free Space from Monocular Video. Proceedings of the 2015 IEEE Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2015.62"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016, January 27\u201330). The Cityscapes Dataset for Semantic Urban Scene Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., and Torralba, A. (2017, January 21\u201326). Scene parsing through ade20k dataset. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.544"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., and Nie\u00dfner, M. (2017, January 21\u201326). ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. Proceedings of the Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.261"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., and Darrell, T. (2020, January 13\u201319). BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_33","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 23\u201328). The Role of Context for Object Detection and Semantic Segmentation in the Wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.119"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Taipalmaa, J., Passalis, N., Zhang, H., Gabbouj, M., and Raitoharju, J. (2019, January 13\u201316). High-Resolution Water Segmentation for Autonomous Unmanned Surface Vehicles: A Novel Dataset and Evaluation. Proceedings of the 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), Pittsburgh, PA, USA.","DOI":"10.1109\/MLSP.2019.8918694"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TITS.2016.2634580","article-title":"Video processing from electro-optical sensors for object detection and tracking in a maritime environment: A survey","volume":"18","author":"Prasad","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1177\/0278364917751842","article-title":"The Visual\u2013Inertial Canoe Dataset","volume":"37","author":"Miller","year":"2018","journal-title":"Int. J. Robot. Res."},{"key":"ref_38","unstructured":"Lambert, R., Li, J., Chavez-Galaviz, J., Wang, Z., and Mahmoudian, N. (2022, April 28). River Obstacle Segmentation En-Route by USV Dataset (ROSEBUD). Purdue University Research Repository. Available online: https:\/\/purr.purdue.edu\/publications\/4072\/1."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lambert, R., Page, B., Chavez, J., and Mahmoudian, N. (2020, January 5\u201330). A Low-Cost Autonomous Surface Vehicle for Multi-Vehicle Operations. Proceedings of the Global Oceans 2020: Singapore\u2014U.S. Gulf Coast, Biloxi, MS, USA.","DOI":"10.1109\/IEEECONF38699.2020.9389236"},{"key":"ref_40","unstructured":"Taipalmaa, J. (2022, April 28). Tampere-WaterSeg. Version 1. Available online: http:\/\/urn.fi\/urn:nbn:fi:att:eafdb99c-4396-4591-80e0-24219875b5b6."},{"key":"ref_41","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 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mehta, S., Rastegari, M., Caspi, A., Shapiro, L., and Hajishirzi, H. (2018, January 8\u201314). Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_34"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_45","unstructured":"Wu, H., Zhang, J., Huang, K., Liang, K., and Yu, Y. (2019). Fastfcn: Rethinking dilated convolution in the backbone for semantic segmentation. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and Sang, N. (2018, January 8\u201314). Bisenet: Bilateral segmentation network for real-time semantic segmentation. Proceedings of the European conference on computer vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"\u017dust, L., and Kristan, M. (2022, January 3\u20138). Learning Maritime Obstacle Detection from Weak Annotations by Scaffolding. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00195"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4681\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:36:47Z","timestamp":1760139407000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4681"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,21]]},"references-count":48,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22134681"],"URL":"https:\/\/doi.org\/10.3390\/s22134681","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,21]]}}}