Intelligent deep learning-enabled autonomous small ship detection and classification model
Autonomous ship technologies have gained considerable interest due to the minimization of the challenging issues faced by the unpredictable errors of manual navigation, and therefore reduces human labor, increasing navigation security and profit margin. On autonomous shipping technologies, small shi...
- Autores:
-
Escorcia-Gutierrez, Jose
Gamarra, Margarita
BELEÑO SAENZ, KELVIN
Soto, Carlos
Mansour, Romany
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9333
- Acceso en línea:
- https://hdl.handle.net/11323/9333
https://doi.org/10.1016/j.compeleceng.2022.107871
https://repositorio.cuc.edu.co/
- Palabra clave:
- Autonomous systems
Artificial intelligence
Ship detection
Deep learning
Mask RCNN
Parameter optimization
- Rights
- embargoedAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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Intelligent deep learning-enabled autonomous small ship detection and classification model |
| title |
Intelligent deep learning-enabled autonomous small ship detection and classification model |
| spellingShingle |
Intelligent deep learning-enabled autonomous small ship detection and classification model Autonomous systems Artificial intelligence Ship detection Deep learning Mask RCNN Parameter optimization |
| title_short |
Intelligent deep learning-enabled autonomous small ship detection and classification model |
| title_full |
Intelligent deep learning-enabled autonomous small ship detection and classification model |
| title_fullStr |
Intelligent deep learning-enabled autonomous small ship detection and classification model |
| title_full_unstemmed |
Intelligent deep learning-enabled autonomous small ship detection and classification model |
| title_sort |
Intelligent deep learning-enabled autonomous small ship detection and classification model |
| dc.creator.fl_str_mv |
Escorcia-Gutierrez, Jose Gamarra, Margarita BELEÑO SAENZ, KELVIN Soto, Carlos Mansour, Romany |
| dc.contributor.author.spa.fl_str_mv |
Escorcia-Gutierrez, Jose Gamarra, Margarita BELEÑO SAENZ, KELVIN Soto, Carlos Mansour, Romany |
| dc.subject.proposal.eng.fl_str_mv |
Autonomous systems Artificial intelligence Ship detection Deep learning Mask RCNN Parameter optimization |
| topic |
Autonomous systems Artificial intelligence Ship detection Deep learning Mask RCNN Parameter optimization |
| description |
Autonomous ship technologies have gained considerable interest due to the minimization of the challenging issues faced by the unpredictable errors of manual navigation, and therefore reduces human labor, increasing navigation security and profit margin. On autonomous shipping technologies, small ship detection is vital in ensuring shipping safety. With this motivation, this paper presents an efficient optimal mask regional convolutional neural network (Mask-CNN) technique for small ship detection (OMRCNN-SHD) on autonomous shipping technologies. Primarily, the data augmentation process is performed to resolve the issue of the limited number of real-world samples of small ships and helps to detect small ships in most cases accurately. Besides, the Mask RCNN with SqueezeNet model is used to detect ships and the hyperparameter tuning of the SqueezeNet model takes place by the use of the Adagrad optimizer. Furthermore, the Colliding Body's Optimization (CBO) algorithm with the weighted regularized extreme learning machine (WRELM) technique is employed to classify detected ships effectively. The comparative results analysis demonstrates the betterment of the OMRCNN-SHD technique over the current methods with the maximum accuracy of 98.63%. |
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2022 |
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2022-07-05T14:30:18Z |
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2022-07-05T14:30:18Z 2024 |
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2022 |
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José Escorcia-Gutierrez, Margarita Gamarra, Kelvin Beleño, Carlos Soto, Romany F. Mansour, Intelligent deep learning-enabled autonomous small ship detection and classification model, Computers and Electrical Engineering, Volume 100, 2022, 107871, ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2022.107871. |
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0045-7906 |
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https://hdl.handle.net/11323/9333 |
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https://doi.org/10.1016/j.compeleceng.2022.107871 |
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10.1016/j.compeleceng.2022.107871. |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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José Escorcia-Gutierrez, Margarita Gamarra, Kelvin Beleño, Carlos Soto, Romany F. Mansour, Intelligent deep learning-enabled autonomous small ship detection and classification model, Computers and Electrical Engineering, Volume 100, 2022, 107871, ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2022.107871. 0045-7906 10.1016/j.compeleceng.2022.107871. Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/9333 https://doi.org/10.1016/j.compeleceng.2022.107871 https://repositorio.cuc.edu.co/ |
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eng |
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Computers and Electrical Engineering |
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Chen Z, Chen D, Zhang Y, Cheng X, Zhang M, Wu C. Deep learning for autonomous ship-oriented small ship detection. Saf Sci 2020;130:104812. Tran T, Le T. Vision based boat detection for maritime surveillance. In: International Conference on Electronics, Information, and Communications. IEEE; 2016. p. 1–4. Wackerman CC, Friedman KS, Pichel WG, Clemente-Col ONP, Li X. Automatic detection of ships in RADARSAT-1 SAR imagery. Can J Remote Sens 2001;27(5): 568–77. Wijnhoven R, van Rens K, Jaspers EG, de With PH. Online learning for ship detection in maritime surveillance. In: rocceedings of 31th Symposium on Information Theory in the Benelux; 2010. p. 73–80. Yang X, Sun H, Fu K, Yang J, Sun X, Yan M, Guo Z. Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks. Remote Sens 2018;10(1):132. Mansour R, Escorcia-Gutierrez J, Gamarra M, Villanueva J, Leal N. Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model. Image Vision Comput 2020;112:104229. Zurek E, Gamarra M, Escorcia-Gutierrez J, Gutierrez C, Bayona H. A robust application in vessel recognition based on neural classification of acoustic fingerprint. Int J Artif Intell 2018;16(1):195–213. Yao Y, Jiang Z, Zhang H, Zhao D, Cai B. Ship detection in optical remote sensing images based on deep convolutional neural networks. J Appl Remote Sens 2017; 11(4):042611. Zhang X, Wang H, Xu C, Lv Y, Fu C, Xiao H, He Y. A lightweight feature optimizing network for ship detection in SAR image. IEEE Access 2019;7:141662–78. Fan Q, Chen F, Cheng M, Lou S, Xiao R, Zhang B, Wang C, Li J. Ship detection using a fully convolutional network with compact polarimetric SAR images. Remote Sens 2019;11:2171. Fu J, Sun X, Wang Z, Fu K. An anchor-free method based on feature balancing and refinement network for multiscale ship detection in SAR images. IEEE Trans Geosci Remote Sens 2020. Fu J, Sun X, Wang Z, Fu K. An anchor-free method based on feature balancing and refinement network for multiscale ship detection in SAR images. IEEE Trans Geosci Remote Sens 2020. Guo H, Yang X, Wang N, Gao X. A CenterNet++ model for ship detection in SAR images. Pattern Recognit 021;112:107787. Chen P, Li Y, Zhou H, Liu B, Liu P. Detection of small ship objects using anchor boxes cluster and feature pyramid network model for SAR imagery. J Mar Sci Eng 2020;8(2):112. Nina W, Condori W, Machaca V, Villegas J, Castro E. Small ship detection on optical satellite imagery with YOLO and YOLT. In: Future of Information and Communication Conference. Cham: Springer; 2020. p. 664–77. Jin K, Chen Y, Xu B, Yin J, Wang X, Yang J. A patch-to-pixel convolutional neural network for small ship detection with PolSAR Images. IEEE Trans Geosci Remote Sens 2020;58(9):6623–38. Wang J, Lin Y, Guo J, Zhuang L. SSS-YOLO: towards more accurate detection for small ships in SAR image. Remote Sens Lett 2021;12(2):93–102. Devadharshini S, Kalaipriya R, Rajmohan R, Pavithra M, Ananthkumar T. Performance investigation of Hybrid YOLO-VGG16 based ship detection framework using SAR images. In: 2020 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE; 2020. p. 1–6. Liu Y, Cui HY, Kuang Z, Li GQ. Ship detection and classification on optical remote sensing images using deep learning. In: ITM Web of Conferences. EDP Sciences; 2017. p. 05012. Vol. 12. Yu Y, Zhang K, Yang L, Zhang D. Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN. Comput Electron Agric 2019;163:104846. Xu Y, Yang G, Luo J, He J. An Electronic component recognition algorithm based on deep learning with a faster queezeNet. Math Probl Eng 2020:2020. Zhang C, Yao M, Chen W, Zhang S, Chen D, Wu Y. Gradient descent optimization in deep learning model training based on multistage and method combination strategy. In: Security and Communication Networks; 2021. p. 2021. Wang Y, Zhou G. The novel successive variational mode decomposition and weighted regularized extreme learning machine for fault diagnosis of automobile gearbox. Shock Vib 2021:2021. Kaveh A, Mahdavi VR. Colliding bodies optimization: a novel meta-heuristic method. Comput Struct 2014;139:18–27. J. Escorcia-Gutierrez et al. |
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Escorcia-Gutierrez, JoseGamarra, MargaritaBELEÑO SAENZ, KELVINSoto, CarlosMansour, Romany2022-07-05T14:30:18Z20242022-07-05T14:30:18Z2022José Escorcia-Gutierrez, Margarita Gamarra, Kelvin Beleño, Carlos Soto, Romany F. Mansour, Intelligent deep learning-enabled autonomous small ship detection and classification model, Computers and Electrical Engineering, Volume 100, 2022, 107871, ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2022.107871.0045-7906https://hdl.handle.net/11323/9333https://doi.org/10.1016/j.compeleceng.2022.10787110.1016/j.compeleceng.2022.107871.Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Autonomous ship technologies have gained considerable interest due to the minimization of the challenging issues faced by the unpredictable errors of manual navigation, and therefore reduces human labor, increasing navigation security and profit margin. On autonomous shipping technologies, small ship detection is vital in ensuring shipping safety. With this motivation, this paper presents an efficient optimal mask regional convolutional neural network (Mask-CNN) technique for small ship detection (OMRCNN-SHD) on autonomous shipping technologies. Primarily, the data augmentation process is performed to resolve the issue of the limited number of real-world samples of small ships and helps to detect small ships in most cases accurately. Besides, the Mask RCNN with SqueezeNet model is used to detect ships and the hyperparameter tuning of the SqueezeNet model takes place by the use of the Adagrad optimizer. Furthermore, the Colliding Body's Optimization (CBO) algorithm with the weighted regularized extreme learning machine (WRELM) technique is employed to classify detected ships effectively. The comparative results analysis demonstrates the betterment of the OMRCNN-SHD technique over the current methods with the maximum accuracy of 98.63%.13 páginasapplication/pdfengElsevier Ltd.United KingdomAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© 2022 Elsevier Ltd. All rights reserved.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfIntelligent deep learning-enabled autonomous small ship detection and classification modelArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85https://www.sciencedirect.com/science/article/pii/S0045790622001616#!Computers and Electrical EngineeringChen Z, Chen D, Zhang Y, Cheng X, Zhang M, Wu C. Deep learning for autonomous ship-oriented small ship detection. Saf Sci 2020;130:104812.Tran T, Le T. Vision based boat detection for maritime surveillance. In: International Conference on Electronics, Information, and Communications. IEEE; 2016. p. 1–4.Wackerman CC, Friedman KS, Pichel WG, Clemente-Col ONP, Li X. Automatic detection of ships in RADARSAT-1 SAR imagery. Can J Remote Sens 2001;27(5): 568–77.Wijnhoven R, van Rens K, Jaspers EG, de With PH. Online learning for ship detection in maritime surveillance. In: rocceedings of 31th Symposium on Information Theory in the Benelux; 2010. p. 73–80.Yang X, Sun H, Fu K, Yang J, Sun X, Yan M, Guo Z. Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks. Remote Sens 2018;10(1):132.Mansour R, Escorcia-Gutierrez J, Gamarra M, Villanueva J, Leal N. Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model. Image Vision Comput 2020;112:104229.Zurek E, Gamarra M, Escorcia-Gutierrez J, Gutierrez C, Bayona H. A robust application in vessel recognition based on neural classification of acoustic fingerprint. Int J Artif Intell 2018;16(1):195–213.Yao Y, Jiang Z, Zhang H, Zhao D, Cai B. Ship detection in optical remote sensing images based on deep convolutional neural networks. J Appl Remote Sens 2017; 11(4):042611.Zhang X, Wang H, Xu C, Lv Y, Fu C, Xiao H, He Y. A lightweight feature optimizing network for ship detection in SAR image. IEEE Access 2019;7:141662–78.Fan Q, Chen F, Cheng M, Lou S, Xiao R, Zhang B, Wang C, Li J. Ship detection using a fully convolutional network with compact polarimetric SAR images. Remote Sens 2019;11:2171.Fu J, Sun X, Wang Z, Fu K. An anchor-free method based on feature balancing and refinement network for multiscale ship detection in SAR images. IEEE Trans Geosci Remote Sens 2020.Fu J, Sun X, Wang Z, Fu K. An anchor-free method based on feature balancing and refinement network for multiscale ship detection in SAR images. IEEE Trans Geosci Remote Sens 2020.Guo H, Yang X, Wang N, Gao X. A CenterNet++ model for ship detection in SAR images. Pattern Recognit 021;112:107787.Chen P, Li Y, Zhou H, Liu B, Liu P. Detection of small ship objects using anchor boxes cluster and feature pyramid network model for SAR imagery. J Mar Sci Eng 2020;8(2):112.Nina W, Condori W, Machaca V, Villegas J, Castro E. Small ship detection on optical satellite imagery with YOLO and YOLT. In: Future of Information and Communication Conference. Cham: Springer; 2020. p. 664–77.Jin K, Chen Y, Xu B, Yin J, Wang X, Yang J. A patch-to-pixel convolutional neural network for small ship detection with PolSAR Images. IEEE Trans Geosci Remote Sens 2020;58(9):6623–38.Wang J, Lin Y, Guo J, Zhuang L. SSS-YOLO: towards more accurate detection for small ships in SAR image. Remote Sens Lett 2021;12(2):93–102.Devadharshini S, Kalaipriya R, Rajmohan R, Pavithra M, Ananthkumar T. Performance investigation of Hybrid YOLO-VGG16 based ship detection framework using SAR images. In: 2020 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE; 2020. p. 1–6.Liu Y, Cui HY, Kuang Z, Li GQ. Ship detection and classification on optical remote sensing images using deep learning. In: ITM Web of Conferences. EDP Sciences; 2017. p. 05012. Vol. 12.Yu Y, Zhang K, Yang L, Zhang D. Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN. Comput Electron Agric 2019;163:104846.Xu Y, Yang G, Luo J, He J. An Electronic component recognition algorithm based on deep learning with a faster queezeNet. Math Probl Eng 2020:2020.Zhang C, Yao M, Chen W, Zhang S, Chen D, Wu Y. Gradient descent optimization in deep learning model training based on multistage and method combination strategy. In: Security and Communication Networks; 2021. p. 2021.Wang Y, Zhou G. The novel successive variational mode decomposition and weighted regularized extreme learning machine for fault diagnosis of automobile gearbox. Shock Vib 2021:2021.Kaveh A, Mahdavi VR. Colliding bodies optimization: a novel meta-heuristic method. Comput Struct 2014;139:18–27. J. Escorcia-Gutierrez et al.131107871100Autonomous systemsArtificial intelligenceShip detectionDeep learningMask RCNNParameter optimizationPublicationORIGINALIntelligent deep learning-enabled autonomous small ship detection and classification model.pdfIntelligent deep learning-enabled autonomous small ship detection and classification model.pdfapplication/pdf12766535https://repositorio.cuc.edu.co/bitstreams/17bc572e-833d-4586-a858-f30e863cc6f6/download54c8b2ac2d122ee4902e760fef9f670aMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/3a75bed3-9f2f-43dc-9d4a-84c8c8652e60/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTIntelligent deep learning-enabled autonomous small ship detection and classification model.pdf.txtIntelligent deep learning-enabled autonomous small ship detection and classification 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