{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:45:40Z","timestamp":1772300740330,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Camera systems support the rapid assessment of ship traffic at ports, allowing for a better perspective of the maritime situation. However, optimal ship monitoring requires a level of automation that allows personnel to keep track of relevant variables in the maritime situation in an understandable and visualisable format. It therefore becomes important to have real-time recognition of ships present at the infrastructure, with their class and geographic position presented to the maritime situational awareness operator. This work presents a novel dataset, ShipSG, for the segmentation and georeferencing of ships in maritime monitoring scenes with a static oblique view. Moreover, an exploration of four instance segmentation methods, with a focus on robust (Mask-RCNN, DetectoRS) and real-time performances (YOLACT, Centermask-Lite) and their generalisation to other existing maritime datasets, is shown. Lastly, a method for georeferencing ship masks is proposed. This includes an automatic calculation of the pixel of the segmented ship to be georeferenced and the use of a homography to transform this pixel to geographic coordinates. DetectoRS provided the highest ship segmentation mAP of 0.747. The fastest segmentation method was Centermask-Lite, with 40.96 FPS. The accuracy of our georeferencing method was (22 \u00b1 10) m for ships detected within a 400 m range, and (53 \u00b1 24) m for ships over 400 m away from the camera.<\/jats:p>","DOI":"10.3390\/s22072713","type":"journal-article","created":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T21:23:55Z","timestamp":1648848235000},"page":"2713","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Ship Segmentation and Georeferencing from Static Oblique View Images"],"prefix":"10.3390","volume":"22","author":[{"given":"Borja","family":"Carrillo-Perez","sequence":"first","affiliation":[{"name":"German Aerospace Center (DLR), Institute for the Protection of Maritime Infrastructures, Fischkai 1, 27572 Bremerhaven, Germany"}]},{"given":"Sarah","family":"Barnes","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Institute for the Protection of Maritime Infrastructures, Fischkai 1, 27572 Bremerhaven, Germany"}]},{"given":"Maurice","family":"Stephan","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Institute for the Protection of Maritime Infrastructures, Fischkai 1, 27572 Bremerhaven, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,1]]},"reference":[{"key":"ref_1","first-page":"123","article-title":"ResilienceN\u2014A multi-dimensional challenge for maritime infrastructures","volume":"65","author":"Engler","year":"2018","journal-title":"NA\u0160E MORE Znanstveni \u010dasopis za More i Pomorstvo"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, K., Liang, M., Li, Y., Liu, J., and Liu, R.W. (2019, January 15\u201318). Maritime traffic data visualization: A brief review. Proceedings of the 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA), Suzhou, China.","DOI":"10.1109\/ICBDA.2019.8713227"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102271","DOI":"10.1016\/j.apor.2020.102271","article-title":"Exploring AIS data for intelligent maritime routes extraction","volume":"101","author":"Yan","year":"2020","journal-title":"Appl. Ocean. Res."},{"key":"ref_4","unstructured":"(2022, February 16). United States Coast Guard AIS Encoding Guide, Available online: https:\/\/www.navcen.uscg.gov\/pdf\/AIS\/AISGuide.pdf."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jakovlev, S., Daranda, A., Voznak, M., Lektauers, A., Eglynas, T., and Jusis, M. (2020, January 15\u201316). Analysis of the Possibility to Detect Fake Vessels in the Automatic Identification System. Proceedings of the 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), Riga, Latvia.","DOI":"10.1109\/ITMS51158.2020.9259293"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Struck, M.C., and Stoppe, J. (2021, January 26\u201328). A Backwards Compatible Approach to Authenticate Automatic Identification System Messages. Proceedings of the 2021 IEEE International Conference on Cyber Security and Resilience (CSR), Rhodes, Greece.","DOI":"10.1109\/CSR51186.2021.9527954"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wimpenny, G., Safar, J., Grant, A., Bransby, M., and Ward, N. (2018, January 24\u201328). Public key authentication for AIS and the VHF data exchange system (VDES). Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018), Miami, FL, USA.","DOI":"10.33012\/2018.15948"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Alincourt, E., Ray, C., Ricordel, P.M., Dare-Emzivat, D., and Boudraa, A. (2016, January 10\u201313). Methodology for AIS signature identification through magnitude and temporal characterization. Proceedings of the OCEANS 2016-Shanghai, Shanghai, China.","DOI":"10.1109\/OCEANSAP.2016.7485420"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Balduzzi, M., Pasta, A., and Wilhoit, K. (2014, January 8\u201312). A security evaluation of AIS automated identification system. Proceedings of the 30th Annual Computer Security Applications Conference, New Orleans, LA, USA.","DOI":"10.1145\/2664243.2664257"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1017\/S0373463320000326","article-title":"Causal factors and symptoms of task-related human fatigue in vessel traffic service: A task-driven approach","volume":"73","author":"Li","year":"2020","journal-title":"J. Navig."},{"key":"ref_11","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). Microsoft coco: Common objects in context. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"2593","DOI":"10.1109\/TMM.2018.2865686","article-title":"Seaships: A large-scale precisely annotated dataset for ship detection","volume":"20","author":"Shao","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7194342","DOI":"10.1155\/2020\/7194342","article-title":"Video-based detection infrastructure enhancement for automated ship recognition and behavior analysis","volume":"2020","author":"Chen","year":"2020","journal-title":"J. Adv. Transp."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Qiao, S., Chen, L.C., and Yuille, A. (2021, January 20\u201325). Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01008"},{"key":"ref_17","unstructured":"Bolya, D., Zhou, C., Xiao, F., and Lee, Y.J. (November, January 27). Yolact: Real-time instance segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lee, Y., and Park, J. (2020, January 13\u201319). Centermask: Real-time anchor-free instance segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01392"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhang, W., Sun, H., and Xue, B. (2019). Embedded Deep Learning for Ship Detection and Recognition. Future Internet, 11.","DOI":"10.3390\/fi11020053"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"266-1","DOI":"10.2352\/ISSN.2470-1173.2019.11.IPAS-266","article-title":"Multi-class detection and orientation recognition of vessels in maritime surveillance","volume":"2019","author":"Ghahremani","year":"2019","journal-title":"Electron. Imaging"},{"key":"ref_21","first-page":"1154306","article-title":"CNN-based object detection and segmentation for maritime domain awareness","volume":"Volume 11543","author":"Nita","year":"2020","journal-title":"Artificial Intelligence and Machine Learning in Defense Applications II"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s10846-010-9442-7","article-title":"Geolocation of multiple targets from airborne video without terrain data","volume":"62","author":"Han","year":"2011","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"014510","DOI":"10.1117\/1.JRS.14.014510","article-title":"Distortion measurement and geolocation error correction for high altitude oblique imaging using airborne cameras","volume":"14","author":"Cai","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"El Habchi, A., Moumen, Y., Zerrouk, I., Khiati, W., Berrich, J., and Bouchentouf, T. (2020, January 21\u201323). CGA: A New Approach to Estimate the Geolocation of a Ground Target from Drone Aerial Imagery. Proceedings of the 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS), Fez, Morocco.","DOI":"10.1109\/ICDS50568.2020.9268749"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6349","DOI":"10.1109\/TIP.2021.3093789","article-title":"MGG: Monocular Global Geolocation for Outdoor Long-Range Targets","volume":"30","author":"Gao","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108630","DOI":"10.1016\/j.measurement.2020.108630","article-title":"Assessment of ship position estimation accuracy based on radar navigation mark echoes identified in an Electronic Navigational Chart","volume":"169","author":"Naus","year":"2020","journal-title":"Measurement"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"109435","DOI":"10.1016\/j.oceaneng.2021.109435","article-title":"An enhanced CNN-enabled learning method for promoting ship detection in maritime surveillance system","volume":"235","author":"Liu","year":"2021","journal-title":"Ocean. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103520","DOI":"10.1016\/j.marpol.2019.103520","article-title":"AIS in maritime research","volume":"106","author":"Svanberg","year":"2019","journal-title":"Mar. Policy"},{"key":"ref_29","first-page":"14476","article-title":"Low Altitude Georeferencing for Imaging Sensors in Maritime Tracking","volume":"53","author":"Helgesen","year":"2020","journal-title":"IFAC-Pap."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A flexible new technique for camera calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","unstructured":"Wada, K. (2022, February 16). labelme: Image Polygonal Annotation with Python. Available online: https:\/\/github.com\/wkentaro\/labelme."},{"key":"ref_32","unstructured":"(2022, February 16). Regulation (EU) 2016\/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data, and Repealing Directive 95\/46\/EC (General Data Protection Regulation) (Text with EEA Relevance). Available online: https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=CELEX:32016R0679."},{"key":"ref_33","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_36","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_37","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, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Guo, Y., Chen, F., Cheng, Q., Wu, J., Wang, B., Wu, Y., and Zhao, W. (2020, January 13\u201315). Fully Convolutional One-Stage Circular Object Detector on Medical Images. Proceedings of the 2020 4th International Conference on Advances in Image Processing, Chengdu, China.","DOI":"10.1145\/3441250.3441269"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lee, Y., Hwang, J.w., Lee, S., Bae, Y., and Park, J. (2019, January 16\u201317). An energy and gpu-computation efficient backbone network for real-time object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00103"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Solano-Carrillo, E., Carrillo-Perez, B., Flenker, T., Steiniger, Y., and Stoppe, J. (2021, January 19\u201322). Detection and Geovisualization of Abnormal Vessel Behavior from Video. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA.","DOI":"10.1109\/ITSC48978.2021.9564675"},{"key":"ref_41","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., and Xu, J. (2019). MMDetection: Open MMLab Detection Toolbox and Benchmark. arXiv."},{"key":"ref_42","unstructured":"Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., and Girshick, R. (2022, February 16). Detectron2. Available online: https:\/\/github.com\/facebookresearch\/detectron2."},{"key":"ref_43","unstructured":"Pawlowski, E. (2015, January 17\u201319). Experimental study of a positioning accuracy with GPS receiver. Proceedings of the 12th Conference on Selected Problems of Electrical Engineering and Electronics, WZEZ, Kielce, Poland."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2713\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:48:15Z","timestamp":1760136495000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2713"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,1]]},"references-count":43,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22072713"],"URL":"https:\/\/doi.org\/10.3390\/s22072713","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,1]]}}}