Rip currents form on beaches worldwide and pose a potential safety hazard for beach visitors. Therefore, effectively identifying rip currents from beach scenes and providing real-time alerts to beach managers and beachgoers is crucial. In this study, the YOLO-Rip model was proposed to detect rip current targets based on current popular deep learning techniques. First, based on the characteristics of a large target size in rip current images, the neck region in the YOLOv5s model was streamlined. The 80 × 80 feature map branches suitable for detecting small targets were removed to reduce the number of parameters, decrease the complexity of the model, and improve the real-time detection performance. Subsequently, we proposed adding a joint dilated convolutional (JDC) module to the lateral connection of the feature pyramid network (FPN) to expand the perceptual field, improve feature information utilization, and reduce the number of parameters, while keeping the model compact. Finally, the SimAM module, which is a parametric-free attention mechanism, was added to optimize the target detection accuracy. Several mainstream neural network models have been used to train self-built rip current image datasets. The experimental results show that (i) the detection results from different models using the same dataset vary greatly and (ii) compared with YOLOv5s, YOLO-Rip increased the mAP value by approximately 4% (to 92.15%), frame rate by 2.18 frames per second, and the model size by only 0.46 MB. The modified model improved the detection accuracy while keeping the model streamlined, indicating its efficiency and accuracy in the detection of rip currents.

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Among many coastal hazards, rip currents have gradually become one of the most noticeable hazards. Studies have demonstrated that most drowning accidents at beaches around the world are related to rip currents. In this study, online and field questionnaires were combined for the first time to reveal beachgoers’ awareness of rip currents in China from four aspects: demographic characteristics, swimming ability, information about visiting beaches, and knowledge about rip currents. One educational strategy was introduced to the field survey. The results suggest that (i) the proportion of online and field respondents who have heard of “rip currents” and seen warning signs of rip currents is extremely small. This reflects that beachgoers lack awareness of rip current hazards. Thus, China needs to strengthen the safety education of rip current knowledge. (ii) The level of awareness of rip currents can significantly affect the community’s ability to identify the location of rip currents and their choice of escape direction. (iii) In the field survey, we implemented an educational strategy as an intervention for respondents, and the accuracy of identifying rip currents and choosing the correct escape route improved by 34% and 46.7%, respectively. This implies that the intervention of educational strategy can significantly deepen beachgoers’ awareness of rip currents. It is recommended that more educational strategies about rip current knowledge be implemented on Chinese beaches in the future.

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