Classification of Sailboat Tell Tail Based on Deep Learning

The tell tail is usually placed on the triangular sail to display the running state of the air flow on the sail surface. It is of great significance to make accurate judgement on the drift of the tell tail of the sailboat during sailing for the best sailing effect. Normally it is difficult for sailors to keep an eye for a long time on the tell sail for accurate judging its changes, affected by strong sunlight and visual fatigue. In this case, we adopt computer vision technology in hope of helping the sailors judge the changes of the tell tail in ease with ease. This paper proposes for the first time a method to classify sailboat tell tails based on deep learning and an expert guidance system, supported by a sailboat tell tail classification data set on the expert guidance system of interpreting the tell tails states in different sea wind conditions, including the feature extraction performance. Considering the expression capabilities that vary with the computational features in different visual tasks, the paper focuses on five tell tail computing features, which are recoded by an automatic encoder and classified by a SVM classifier. All experimental samples were randomly divided into five groups, and four groups were selected from each group as the training set to train the classifier. The remaining one group was used as the test set for testing. The highest resolution value of the ResNet network was 80.26%. To achieve better operational results on the basis of deep computing features obtained through the ResNet network in the experiments. The method can be used to assist the sailors in making better judgement about the tell tail changes during sailing.