Ship trajectory self disturbance rejection detection technology based on optical image classification
During the navigation process of ships,natural factors such as wind,waves,and currents,as well as the movement characteristics of the ship itself,can cause the ship to deviate from the predetermined trajectory,affecting the safety and stability of navigation.In order to effectively control the ship's trajectory and ensure the safe operation of the ship,a ship's trajectory self disturbance detection technology based on optical image classification is proposed.Using convolution-al neural networks to extract ship features from optical images,and dividing the optical images of ship navigation targets in-to large and small navigation target slices,the first layer SVM classifier is constructed based on the large navigation target features to train missed large and small navigation target datasets,forming the second and third layers of SVM classifiers.Using this classifier to remove and filter the mined feature parameters layer by layer,detecting missed large and small navig-ation targets,and ultimately identifying and classifying optical image data in ship trajectories;On this basis,using self dis-turbance rejection technology combined with Gaussian kernel mapping,complex changes in ship trajectories are captured to achieve self disturbance rejection detection of ship trajectories.The experimental results show that the method can effect-ively distinguish the target optical image,and can complete the ship track detection under interference,so as to ensure the safety and stability of ship navigation.
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