The invention discloses an intelligent ship self-change collision avoidance decision-making method based on criterion-oriented visual supervision. The method comprises the following steps: constructing a synthetic data set with labels for intelligent ship collision avoidance visual supervision learning based on criterion guidance; a deep convolution model is trained based on the synthetic data set, the deep convolution model comprises a deep feature extraction layer and a full-connection output layer, the deep feature extraction layer is used for obtaining image features of a ship real-time motion image, the full-connection output layer is used for mapping the image features to three categories of a collision avoidance strategy, and a collision avoidance strategy is obtained; the three categories of the collision avoidance strategy respectively represent left turning, right turning and straight going, and are represented by labels 0, 1 and 2; and acquiring a real-time motion image of the ship, and performing intelligent ship self-change collision avoidance decision through the deep convolution model. According to the method, the adaptive collision avoidance decision can be finally displayed in meeting scenes with different complexity degrees, retraining is not needed, the learning cost is greatly reduced, and the performance is improved.
本发明公开了一种基于准则导向视觉监督的智能船舶自变通避碰决策方法,包括:构建基于准则导向下智能船舶避碰视觉监督学习的带有标注的合成数据集;基于所述合成数据集训练深层次卷积模型,所述深层次卷积模型包括深层次特征提取层和全连接输出层,所述深层次特征提取层用于获取船舶实时运动图像的图像特征,所述全连接输出层用于将图像特征映射到避碰策略的三个类别中,所述避碰策略的三个类别分别表示左转、右转和直行,通过标签0、1和2代表;获取船舶实时运动图像,通过所述深层次卷积模型进行智能船舶自变通避碰决策。本发明最终可以在不同复杂程度的会遇场景中展现出自适应避碰决策,无需重新训练,大幅降低了学习成本,提升了性能。
Intelligent ship self-change collision avoidance decision-making method based on criterion-oriented visual supervision
基于准则导向视觉监督的智能船舶自变通避碰决策方法
10.05.2024
Patent
Elektronische Ressource
Chinesisch
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