Development of Prediction System for Ship Movements Using Machine Learning and Radar Images
Development of Prediction System for Ship Movements Using Machine Learning and Radar Images
复制标题
利用机器学习和雷达图像开发船舶运动预测系统
DOI：
10.1109/smc53654.2022.9945113
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Kobayashi Mitsuru
中科院分区：
文献类型：
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作者：
Nishizaki Chihiro;Takenaka Masako;Hirai Yurie;Okazaki Tadatsugi;Kobayashi Mitsuru
为了保证船舶航行安全，导航员必须了解和预测其他船舶的运动。雷达图像包括船舶（目标）和非船舶（噪声）图像，如海杂波。为了了解其他船舶的运动，导航员必须手动从雷达图像中检测船舶图像。为了在减少工作量的同时保持导航员的最佳态势感知，需要在雷达上自动检测和跟踪船舶图像（包括小型船舶）的功能。因此，本研究提出了一种利用机器学习和雷达图像预测船舶运动的系统。所提出的预测系统是基于使用去噪卷积自编码器开发的学习模型。学习和验证数据是通过图像预处理处理后的雷达图像。本研究船舶运动预测精度为90.97%，损失为0.0396。
To maintain ship navigation safety, the navigator must understand and predict the movements of other ships. Radar images include both ship (target) and non-ship images (noise), such as sea clutter. To understand the movements of other ships, navigators must detect ship images from radar images manually. To maintain the optimum situation awareness of navigators while reducing the workload, a function that can automatically detect and track ship images, including small ships, is desired on the radar. Therefore, a system to predict ship movements using machine learning and radar images is proposed in this study. The proposed prediction system is based on a learning model developed using a denoising convolutional autoencoder. The learning and validation data are radar images processed via image processing in advance. The prediction accuracy of ship movements in this study is 90.97%, and the loss is 0.0396.
至少与载人航运一样安全、安全和“人为错误”吗？
DOI：
10.1201/9781351174664-52
发表时间：
2018
期刊：
Safety and Reliability – Safe Societies in a Changing World
影响因子：
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作者：
T. Porathe;Åsa S. Hoem;Ø. Rødseth;K. Fjørtoft;S. Johnsen
通讯作者：
S. Johnsen