[1. Wawrzyniak, N.; Stateczny, A. Automatic watercraft recognition and identification on water areas covered by video monitoring as extension for sea and river traffic supervision systems. Polish Marit. Res. 2018, 25, 5–13, doi: 10.2478/pomr-2018-0016.10.2478/pomr-2018-0016]Search in Google Scholar
[2. Kanjir, U.; Greidanus, H.; Oštir, K. Vessel detection and classification from spaceborne optical images: A literature survey. Remote Sens. Environ. 2018, 207, 1–26, doi: 10.1016/j. rse.2017.12.033.10.1016/j.rse.2017.12.033]Search in Google Scholar
[3. Bobkowska, K. Analysis of the objects images on the sea using Dempster-Shafer theory. In 2016 17th Int. Radar Symp. (IRS); 2016; pp. 78–81, doi: 10.1109/irs.2016.7497280.10.1109/IRS.2016.7497280]Search in Google Scholar
[4. Wang, C.; Jiang, S.; Zhang, H.; Wu, F.; Zhang, B. Ship detection for high-resolution SAR images based on feature analysis. IEEE Geosci. Remote Sens. Lett. 2014, 11, 119–123, doi: 10.1109/LGRS.2013.2248118.10.1109/LGRS.2013.2248118]Search in Google Scholar
[5. Stateczny, A. Full implementation of the River Information Services of border and lower section of the Odra in Poland. In 2016 Baltic Geodetic Congress (BGC Geomatics); 2016; pp. 140–146, doi: 10.1109/BGC.Geomatics.2016.33.10.1109/BGC.Geomatics.2016.33]Search in Google Scholar
[6. Shao, Z.; Wang, L.; Wang, Z.; Du, W.; Wu, W. Saliency-aware convolution neural network for ship detection in surveillance video. IEEE Trans. Circuits Syst. Video Technol. 2019, doi: 10.1109/TCSVT.2019.2897980.10.1109/TCSVT.2019.2897980]Search in Google Scholar
[7. Wawrzyniak, N.; Hyla, T. Automatic ship identification approach for video surveillance systems. In Proceedings of ICONS 2019 The Fourteenth International Conference on Systems, IARIA, Valencia, Spain; 2019; pp. 65–68.]Search in Google Scholar
[8. Wawrzyniak, N.; Hyla, T.; Popik, A. Vessel detection and tracking method based on video surveillance. Sensors (Switzerland) 2019, 19, 23, doi: 10.3390/s19235230.10.3390/s19235230692876731795198]Search in Google Scholar
[9. Ferreira, J. C.; Branquinho, J.; Ferreira, P. C.; Piedade, F. Computer vision algorithms fishing vessel monitoring – Identification of vessel plate number. In International Symposium on Ambient Intelligence; 2017; pp. 9–17.10.1007/978-3-319-61118-1_2]Search in Google Scholar
[10. Bobkowska, K.; Wawrzyniak, N. The Hough transform in the classification process of inland ships. Sci. JOURNALS Marit. Univ. SZCZECIN-ZESZYTY Nauk. Akad. MORSKIEJ W SZCZECINIE 2019, 58, 9–15, doi: 10.17402/331.]Search in Google Scholar
[11. Akiyama, T.; Kobayashi, Y.; Kishigami, J.; Muto, K. CNN-based boat detection model for alert system using surveillance video vamera. In 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE); 2018; pp. 669–670, doi: 10.1109/GCCE.2018.8574704.10.1109/GCCE.2018.8574704]Search in Google Scholar
[12. Zhang, M. M.; Choi, J.; Daniilidis, K.; Wolf, M. T.; Kanan, C. Vais: A dataset for recognizing maritime imagery in the visible and infrared spectrums. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops; 2015; pp. 10–16, doi: 10.1109/CVPRW.2015.7301291.10.1109/CVPRW.2015.7301291]Search in Google Scholar
[13. Solmaz, B.; Gundogdu, E.; Yucesoy, V.; Koç, A.; Alatan, A. A. Fine-grained recognition of maritime vessels and land vehicles by deep feature embedding. IET Comput. Vis. 2018, 12, 1121–1132, doi: 10.1049/iet-cvi.2018.5187.10.1049/iet-cvi.2018.5187]Search in Google Scholar
[14. Zhong, Z.; Jin, L.; Xie, Z. High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps. In 2015 13th International Conference on Document Analysis and Recognition (ICDAR); 2015; pp. 846–850, doi: 10.1109/ICDAR.2015.7333881.10.1109/ICDAR.2015.7333881]Search in Google Scholar
[15. Tang, P.; Wang, H.; Kwong, S. G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition. Neurocomputing 2017, 225, 188–197, doi: 10.1016/j. neucom.2016.11.023.10.1016/j.neucom.2016.11.023]Search in Google Scholar
[16. Al-Qizwini, M.; Barjasteh, I.; Al-Qassab, H.; Radha, H. Deep learning algorithm for autonomous driving using GoogLeNet. In 2017 IEEE Intelligent Vehicles Symposium (IV); 2017; pp. 89–96, doi: 10.1109/IVS.2017.7995703.10.1109/IVS.2017.7995703]Search in Google Scholar
[17. Aswathy, P.; Siddhartha; Mishra, D. Deep GoogLeNet features for visual object tracking. In 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS); 2018; pp. 60–66, doi: 10.1109/ICIINFS.2018.8721317.10.1109/ICIINFS.2018.8721317]Search in Google Scholar
[18. Xie, S.; Zheng, X.; Chen, Y.; Xie, L.; Liu, J.; Zhang, Y.; Yan, J.; Zhu, H.; Hu, Y. Artifact removal using improved GoogLeNet for sparse-view CT reconstruction. Sci. Rep. 2018, 8, 6700, doi: 10.1038/s41598-018-25153-w.10.1038/s41598-018-25153-w592808129712978]Search in Google Scholar
[19. Wu, C.; Wen, W.; Afzal, T.; Zhang, Y.; Chen, Y. A compact DNN: Approaching GoogLeNet-level accuracy of classification and domain adaptation. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017, doi: 10.1109/CVPR.2017.88.10.1109/CVPR.2017.88]Search in Google Scholar
[20. Shin, H.; Roth, H.; Gao, M.; Lu, L.; Xu, Z.; Nogues, I.; Yao, J.; Mollura, D.; Summers, R. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 2016, 35, doi: 10.1109/TMI.2016.2528162 .10.1109/TMI.2016.2528162489061626886976]Search in Google Scholar
[21. Castro, W.; Oblitas, J.; De-La-Torre, M.; Cotrina, C.; Bazán, K.; Avila-George, H. Classification of cape gooseberry fruit according to its level of ripeness using machine learning techniques and different color spaces. IEEE Access 2019, 7, 27389–27400, doi: 10.1109/ACCESS.2019.2898223.10.1109/ACCESS.2019.2898223]Search in Google Scholar
[22. Szymak, P. Recognition of underwater objects using deep learning in Matlab. In International Conference on Applied Mathematics & Computational Science (ICAMCS.NET), 2018, doi: 10.1109/ICAMCS.NET46018.2018.00018.10.1109/ICAMCS.NET46018.2018.00018]Search in Google Scholar
[23. https://www.mathworks.com/help/deeplearning/examples/train-deep-learning-network-to-classify-new-images.html.]Search in Google Scholar
[24. Hyla, T.; Wawrzyniak, N. Automatic ship detection on inland waters: Problems and a preliminary solution. In Proceedings of ICONS 2019 The Fourteenth International Con-ference on Systems, IARIA, Valencia, Spain; 2019; pp. 56–60.]Search in Google Scholar
[25. Popik, A.; Zaniewicz, G.; Wawrzyniak, N. On-water video surveillance: data management for a ship identification system. Zesz. Nauk. Akad. Morskiej w Szczecinie 2019, 60, 56–63, doi: 10.17402/372.]Search in Google Scholar
[26. Wawrzyniak, N.; Hyla, T. Ships detection on inland waters using video surveillance system. In FIP International Conference on Computer Information Systems and Industrial Management; Springer, Cham, 2019; pp. 39–49, doi: 10.1007/978-3-030-28957-7_4.10.1007/978-3-030-28957-7_4]Search in Google Scholar
[27. Tharwat, A. Classification assessment methods. Appl. Comput. Informatics 2018, doi: 10.1016/j.aci.2018.08.003.10.1016/j.aci.2018.08.003]Search in Google Scholar
[28. Wlodarczyk-Sielicka, M.; Polap, D. Automatic Classification Using Machine Learning for Non-Conventional Vessels on Inland Waters. Sensors (Basel). 2019, 19, 3051, doi: 10.3390/s19143051.10.3390/s19143051667876831295955]Search in Google Scholar