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These are our major contributions from this research work. We have introduced a novel dataset for rip current detection, setting a new benchmark in terms of dataset scale. We leverage 110,215 region-specific coastline images both with and without rip current for the first time. We perform a comparative analysis with SoTA object detection models including Faster-RCNN, YOLOv5, and YOLOv8.
Among other models, YOLOv8 emerged as the most effective model and demonstrated its suitability for detecting the amorphous nature of rip currents. We perform a careful data augmentation procedure to avoid over-fitting on specific region images. We implement the XAI model explainability tool named Eigen-CAM to interpret the intensity of rip currents.
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IEEE/ICACT20240312 Slide.17
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