Solving pattern recognition problems in shipping monitoring systems based on CNN-models
Keywords:
convolutional neural networks, pattern recognition, vessel classification, shipping monitoring systems, deep learning, computer vision, feature extraction, optimization, batch normalization, river transport, visual information processing, machine learning, transport security, environmental monitoringSynopsis
The chapter examines modern approaches to solving pattern recognition problems in shipping monitoring systems using convolutional neural network (CNN) models. Traditional rule-based and classical classification methods remain ineffective for complex visual tasks in inland and maritime transport due to high variability of vessel shapes, viewing angles, illumination conditions, and significant intraclass differences. Deep learning architectures – particularly CNNs – are shown to provide robust and scalable solutions by automatically extracting hierarchical features and forming stable, noise-resistant image representations.
The chapter provides a comprehensive review of the fundamental principles of CNN construction, including convolutional layers, feature maps, activation functions, pooling operations, regularization techniques, batch normalization, and optimization strategies. Special attention is devoted to the mechanisms of training neural networks: gradient-based optimization, stochastic gradient descent, hyperparameter tuning, prevention of overfitting, and the use of computational graphs for backpropagation.
Experimental evaluation was conducted using a dataset comprising more than 12,000 annotated images of inland waterway vessels, including tugboats, barges, passenger ships, and dry cargo vessels, captured in real river-port conditions under varying illumination, viewpoints, partial occlusions, and noise. The proposed CNN model achieved an overall classification accuracy of 93.4%, with precision of 92.1%, recall of 91.7%, and an F1-score of 91.9%. Comparative analysis demonstrates that the proposed approach outperforms classical methods such as k-nearest neighbors, linear classifiers, and feature-based support vector machines by 8 – 12% on average.
The practical importance of CNN-based methods for real-time monitoring of river port water areas in Ukraine is emphasized. The proposed models enable automated classification and recognition of surface vessels, enhancing the efficiency, accuracy, and adaptability of intelligent surveillance systems. The results contribute to the development of advanced decision-support tools in maritime transport, improving safety, environmental monitoring, and operational management of shipping flows.
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Pages
43-72
Text Identifier of this Chapter
Chapter 2
Published
June 10, 2026
Copyright (c) 2026 Pavlo Mykhalichenko, Tetiana Cherniavska, Bohdan Cherniavskyi, Viktor Nadtochii, Anatolii Nadtochyi, Maiia Korkh
How to Cite
Mykhalichenko, P., Cherniavska, T., Cherniavskyi, B., Nadtochii, V., Nadtochyi, A., & Korkh, M. (2026). Solving pattern recognition problems in shipping monitoring systems based on CNN-models. In V. Lukin (Ed.), & V. Lukin, S. Kryvenko, F. Li, S. Abramov, V. Abramova, B. Kovalenko, I. Dohtiev, O. Arkhipov, N. Stojanović, B. Bondžulić, P. Mykhalichenko, T. Cherniavska, B. Cherniavskyi, V. Nadtochii, A. Nadtochyi, M. Korkh, Zghurska О., S. Kasian, K. Nakonechna, … I. Kalinichenko, Pattern recognition in surveillance systems and diagnostics (pp. 43-72). Scientific Route OÜ®. https://doi.org/10.21303/978-9908-845-05-0.ch2
