Rip Current Identification in Optical Images Using Wavelet Transform

Rip currents are fast-moving, narrow channels of water that flow seaward from the shoreline, typically forming within the surf zone and extending beyond the wave-breaking region. These currents pose significant hazards to swimmers, contributing to numerous drowning incidents, especially with the increasing popularity of ocean recreation. Despite their prevalence, rip currents remain difficult to detect visually, and no universally reliable method exists for their identification by beachgoers. To address this challenge, this study presents a novel approach for detecting rip currents in optical images using wavelet-based edge detection and image convolution techniques. Five identification criteria were established based on previous literature and expert observations. The proposed program incorporates image augmentation, averaging, and frame aggregation to enhance generalization and accuracy. Experimental analysis involving four iterations and four wavelet bases demonstrated that using two iterations with the Daubechies wavelet yielded the highest interpretation accuracy (88.3%). Performance evaluation using a confusion matrix further confirmed an accuracy rate of 83.0%. The results indicate that the proposed method identifies rip currents in images, offering a valuable tool for researchers studying rip current patterns. This approach lays the groundwork for future advancements in rip current detection and related research.