Gdańsk University of Technology, Faculty of Electronics, Telecommunications and Informatics, Department of Teleinformation Networks, Gabriela Narutowicza 11/12, 80-233 Gdańsk, Poland.
Gdańsk University of Technology, Faculty of Electronics, Telecommunications and Informatics, Department of Teleinformation Networks, Gabriela Narutowicza 11/12, 80-233 Gdańsk, Poland; Medical University of Gdańsk, Faculty of Health Sciences, Department of Radiological Informatics and Statistics, Tuwima 15, 80-210 Gdańsk, Poland.
ISA Trans. 2022 Jan;119:1-16. doi: 10.1016/j.isatra.2021.02.030. Epub 2021 Feb 22.
The article concerns the automation of vessel movement anomaly detection for maritime and coastal traffic safety services. Deep Learning techniques, specifically Convolutional Neural Networks (CNNs), were used to solve this problem. Three variants of the datasets, containing samples of vessel traffic routes in relation to the prohibited area in the form of a grayscale image, were generated. 1458 convolutional neural networks with different structures were trained to find the best structure to classify anomalies. The influence of various parameters of network structures on the overall accuracy of classification was examined. For the best networks, class prediction rates were examined. Activations of selected convolutional layers were studied and visualized to present how the network works in a friendly and understandable way. The best convolutional neural network for detecting vessel movement anomalies has been proposed. The proposed CNN is compared with multiple baseline algorithms trained on the same dataset.
这篇文章涉及到用于海上和沿海交通安全服务的船舶运动异常检测自动化。深度学习技术，特别是卷积神经网络（CNN），被用于解决这个问题。生成了三个包含船舶交通路线样本的数据集变体，这些样本以灰度图像的形式与禁止区域有关。训练了 1458 个具有不同结构的卷积神经网络，以找到最佳结构来分类异常。检查了网络结构的各种参数对分类总体准确性的影响。对于最佳网络，检查了类别的预测率。研究并可视化了选定的卷积层的激活情况，以便以友好和易于理解的方式展示网络的工作原理。已经提出了用于检测船舶运动异常的最佳卷积神经网络。所提出的 CNN 与在同一数据集上训练的多个基线算法进行了比较。
