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.

激流在世界各地的海滩上形成，对海滩游客构成潜在的安全隐患。因此，有效识别海滩场景中的离岸流并向海滩管理者和海滩游客提供实时警报至关重要。在这项研究中，基于当前流行的深度学习技术，提出了 YOLO-Rip 模型来检测 rip 当前目标。首先，基于rip current图像中目标尺寸大的特点，对YOLOv5s模型中的颈部区域进行了精简。去除了适合检测小目标的80×80特征图分支，以减少参数数量，降低模型复杂度，提高实时检测性能。随后，我们提出在特征金字塔网络（FPN）的横向连接中添加联合扩张卷积（JDC）模块，以扩大感知场，提高特征信息利用率，减少参数数量，同时保持模型紧凑。最后，添加了 SimAM 模块，这是一种无参数注意机制，以优化目标检测精度。几种主流的神经网络模型已被用于训练自建的 rip 当前图像数据集。实验结果表明（i）使用相同数据集的不同模型的检测结果差异很大，（ii）与 YOLOv5s 相比，YOLO-Rip 提高了 添加了一种无参数注意机制，以优化目标检测精度。几种主流的神经网络模型已被用于训练自建的 rip 当前图像数据集。实验结果表明（i）使用相同数据集的不同模型的检测结果差异很大，（ii）与 YOLOv5s 相比，YOLO-Rip 提高了 添加了一种无参数注意机制，以优化目标检测精度。几种主流的神经网络模型已被用于训练自建的 rip 当前图像数据集。实验结果表明（i）使用相同数据集的不同模型的检测结果差异很大，（ii）与 YOLOv5s 相比，YOLO-Rip 提高了地图值大约提高了 4%（达到 92.15%），帧速率提高了 2.18 帧/秒，模型大小只有 0.46 MB。修改后的模型在保持模型流线型的同时提高了检测精度，表明其在检测裂口电流方面的效率和准确性。