Ship-at-Sea Vessel Detection and Tracking with a Multi-Point Fuzzy Model for the Deep Learning Classification Process
Keywords:
Vessel Detection, Deep Learning, Fuzzy Model, Multi-Point tracking, Probabilistic ClassificationAbstract
Efficient detection and tracking of small-scale vessels in ships at sea hold significant importance for various maritime applications, including safety, security, and resource management. This research paper designed a framework to address these challenges by employing deep learning algorithms in combination with the innovative Fuzzy Multi-Point Tracking Probabilistic Classifier (FMPTPC) for precise vessel detection and tracking small-scale vessels in ships at sea. The proposed FMPTPC model. The primary integrates deep learning techniques for vessel detection and the introduction of the FMPTPC as a novel classifier for tracking. Deep learning, utilizing convolutional neural networks (CNNs), aids in the detection of vessels in complex maritime environments, while the FMPTPC enhances tracking accuracy by considering multiple data points and applying fuzzy logic for probabilistic classification. With the FMPTPC initial vessel detection, the FMPTPC refines tracking by probabilistically classifying and tracking vessels based on various imagery data acquired with vision at ships at sea. Through the utilized fuzzy rules deep learning architecture is trained and tested for the validation. The analysis of the results expressed that the proposed FMPTPC model achieves a higher accuracy of 0.99 for robust vessel detection and tracking, essential for applications such as search and rescue, fisheries management, and border security. This paper is widespread adoption of deep learning algorithms integrated with the FMPTPC in ship-at-sea environments, where small-scale vessel detection and tracking are essential. This integration represents a significant advancement in the fields of maritime security, surveillance, and resource management, promising improved safety and security in waters.
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Zhang, X., Wang, C., Jiang, L., An, L., & Yang, R. (2021). Collision-avoidance navigation systems for Maritime Autonomous Surface Ships: A state of the art survey. Ocean Engineering, 235, 109380.
Panda, J. P. (2023). Machine learning for naval architecture, ocean and marine engineering. Journal of Marine Science and Technology, 28(1), 1-26.
Wu, S., Li, X., Dong, W., Wang, S., Zhang, X., & Xu, Z. (2023). Multi-source and heterogeneous marine hydrometeorology spatio-temporal data analysis with machine learning: a survey. World Wide Web, 26(3), 1115-1156.
He, W., Liu, X., Chu, X., Wang, Z., Fracz, P., & Li, Z. (2021). A Novel Fitting Model for Practical AIS Abnormal Data Repair in Inland River. Elektronika ir Elektrotechnika, 27(1), 60-70.
Min, S., Jeong, K., Noh, Y., Won, D., & Kim, S. (2022). Damage detection for tethers of submerged floating tunnels based on convolutional neural networks. Ocean Engineering, 250, 111048.
Liu, Y., Li, W., Lin, S., Zhou, X., & Ge, Y. (2023). Hydraulic system fault diagnosis of the chain jacks based on multi-source data fusion. Measurement, 113116.
Yuan, J., Mao, W., Hu, C., Zheng, J., Zheng, D., & Yang, Y. (2023). Leak detection and localization techniques in oil and gas pipeline: A bibliometric and systematic review. Engineering Failure Analysis, 107060.
Palwankar, M. P. (2022). Automation and Expert System Framework for Coupled Shell-Solid Finite Element Modeling of Complex Structures (Doctoral dissertation, Virginia Tech).
Lu, X., Li, Y., & Xie, M. (2023). Preliminary study for motion pose of inshore ships based on point cloud: Estimation of ship berthing angle. Measurement, 214, 112836.
Wu, L., Xiang, Z., Shu, D., Liu, M., Yang, J., & Li, M. (2023). Dynamic Inversion Model of the Mooring Force on a Floating Bollard of a Sea Lock. Journal of Marine Science and Engineering, 11(7), 1374.
Kaizer, A., & Neumann, T. (2021). The model of support for the decision-making process, while organizing dredging works in the ports. Energies, 14(9), 2706.
Ma, R., Chai, X., Geng, R., Xu, L., Xie, R., Zhou, Y., ... & Gao, F. (2023). Recent progress and challenges of multi-stack fuel cell systems: Fault detection and reconfiguration, energy management strategies, and applications. Energy Conversion and Management, 285, 117015.
Jin, Y., Wu, H., Zheng, J., Zhang, J., & Liu, Z. (2023). Power Transformer Fault Diagnosis Based on Improved BP Neural Network. Electronics, 12(16), 3526.
Kumar, D. P., Muralidharan, V., & Ravikumar, S. (2022). Histogram as features for fault detection of multi point cutting tool–A data driven approach. Applied Acoustics, 186, 108456.
Wang, X., Yu, S., Li, S., & Zhang, N. (2022). Two parameter optimization methods of multi-point geostatistics. Journal of Petroleum Science and Engineering, 208, 109724.
Civilibal, S., Cevik, K. K., & Bozkurt, A. (2023). A deep learning approach for automatic detection, segmentation and classification of breast lesions from thermal images. Expert Systems with Applications, 212, 118774.
Zhang, J., Pan, L., Han, Q. L., Chen, C., Wen, S., & Xiang, Y. (2021). Deep learning based attack detection for cyber-physical system cybersecurity: A survey. IEEE/CAA Journal of Automatica Sinica, 9(3), 377-391.
Zou, Z., Long, T., Wang, Q., Wang, L., Chen, J., Zou, B., & Xu, L. (2022). Implementation of Apple’s automatic sorting system based on machine learning. Food Science and Technology, 42, e24922.
He, Q., Yu, F., Chang, J., & Ouyang, C. (2023). Fuzzy granular recurrence plot and quantification analysis: A novel method for classification. Pattern Recognition, 139, 109456.
Waheed, M., Javeed, M., & Jalal, A. (2021, November). A novel deep learning model for understanding two-person interactions using depth sensors. In 2021 International Conference on Innovative Computing (ICIC) (pp. 1-8). IEEE.
Waqas, M., Tahir, M. A., & Khan, S. A. (2023). Robust bag classification approach for multi-instance learning via subspace fuzzy clustering. Expert Systems with Applications, 214, 119113.
Kale, A. P., Wahul, R. M., Patange, A. D., Soman, R., & Ostachowicz, W. (2023). Development of Deep Belief Network for Tool Faults Recognition. Sensors, 23(4), 1872.
Zhou, K., Oh, S. K., Qiu, J., Pedrycz, W., & Seo, K. (2022). Reinforced Two-Stream Fuzzy Neural Networks Architecture Realized With the Aid of One-Dimensional/Two-Dimensional Data Features. IEEE Transactions on Fuzzy Systems, 31(3), 707-721.
Pérez-Ruiz, J. L., Tang, Y., & Loboda, I. (2021). Aircraft engine gas-path monitoring and diagnostics framework based on a hybrid fault recognition approach. Aerospace, 8(8), 232.
Yin, L., & Huang, J. (2023). DResInceptionNasNet method for offline grounding detection of distribution networks. Applied Soft Computing, 110945.
Liu, K. H., Jin, Q. S., Xu, H. C., Gan, Y. S., & Liong, S. T. (2021). Micro-expression recognition using advanced genetic algorithm. Signal Processing: Image Communication, 93, 116153.
Fan, Z., Xu, X., Wang, R., & Wang, H. (2022). CF-HSACNN: A joint anti-noise learning framework for centrifugal fan state recognition. Measurement, 202, 111902.
Li, Y., Zeng, X., & Shi, Y. (2023). A spatial and temporal signal fusion based intelligent event recognition method for buried fiber distributed sensing system. Optics & Laser Technology, 166, 109658.
Wu, S., Li, X., Dong, W., Wang, S., Zhang, X., & Xu, Z. (2023). Multi-source and heterogeneous marine hydrometeorology spatio-temporal data analysis with machine learning: a survey. World Wide Web, 26(3), 1115-1156.
Waykole, S., Shiwakoti, N., & Stasinopoulos, P. (2021). Review on lane detection and tracking algorithms of advanced driver assistance system. Sustainability, 13(20), 11417.
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