Machine learning applications in detecting rip channels from images

Abstract Images containing rip channels are used in oceanographic studies and can be preprocessed for these studies by identifying which regions of the image contain rip channels. For thousands of images, this process can become cumbersome. In recent years, object detection has become a successful approach for identifying regions of an image. There are several different algorithms for detecting objects from images, however, there is no guidance as to which algorithm works well for detecting rip channels. This paper aims to compare and explore state-of-the-art machine learning algorithms, including the Viola–Jones algorithm, convolution neural networks, and a meta-learner on a dataset of rip channel images. Along with the comparison, another objective is to find suitable features for rip channels and to implement the meta-classifier for competition with the state of the art. The comparison suggests the meta-classifier is the most promising detection model. In addition, five new Haar features are found to successfully supplement the original Haar feature set. The final comparison of these models will help guide researchers when choosing an appropriate model for rip channel detection, the new Haar features provide researchers with valuable data for detecting rip channels, and the meta-classifier provides a method for increasing the accuracy of a detector through classifier stacking.