Kelvin wake detection from large-scale optical imagery using simulated data trained deep neural network.

Detecting ship wakes is essential for locating moving vessels at sea. Of the various wake types, Kelvin wakes are particularly intriguing because of the vital information they convey about ships. However, identifying Kelvin wakes is challenging due to their expansive planar distributions and their variable brightness and forms. This paper introduces a deep neural network-based technique specifically tailored for detecting Kelvin wakes in large-scale, high-resolution optical images. After distinguishing between land and water, the entire water region of the image was segmented into overlapping sub-images. GoogLeNet was then employed to differentiate between Kelvin wakes and natural sea surfaces within each sub-image. Regions exhibiting Kelvin wakes were subsequently identified by combining the wake-classified sub-images. Given the limited diversity of available Kelvin wake samples, the training dataset merged true and simulated Kelvin wake images, which acted as positive samples for the deep neural network. The proposed method, when applied to high-resolution optical images, showcased outstanding Kelvin wake detection capabilities, achieving a recall rate of 94.0% and a precision of 70.8%. When detection was limited to the vicinity of ship hulls, the recall, precision, overall accuracy, and specificity achieved remarkable rates of 94.0%, 70.8%, 92.3%, and 94.1% respectively. Furthermore, this research delved into the influence of training samples and input channels on the detection accuracy of wakes. Various DNNs, were utilized for comparison, including AlexNet, VGGNet with 16 layers, GoogLeNet, ResNet with 18 layers, DarkNet with 53 layers, DenseNet with 201 layers. GoogLeNet stood out due to its Inception module, which integrates multi-size convolution kernels, achieving a recall rate of 94.0% and a precision of 70.8%. When detection was limited to the vicinity of ship hulls, the recall, precision, overall accuracy, and specificity achieved remarkable rates of 94.0%, 70.8%, 92.3%, and 94.1% respectively. [Display omitted] •Kelvin wake detection method for large-scale imagery by deep learning was proposed. •The method training and running efficiency was improved with high accuracy. •Simulated Kelvin wakes effectively made up for the lack of diversity wake samples. •Recall, precision, overall accuracy, and specificity were all more than 94.0%. [ABSTRACT FROM AUTHOR]