文献类型：期刊文献

英文题名：YOLO-Rip: A modified lightweight network for Rip currents detection

作者：Zhu, Daoheng[1];Qi, Rui[2];Hu, Pengpeng[1];Su, Qianxin[1];Qin, Xue[2];Li, Zhiqiang[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang, Peoples R China;[2]Guizhou Univ, Sch Big Data & Informat Engn, Guiyang, Peoples R China

年份：2022

卷号：9

外文期刊名：FRONTIERS IN MARINE SCIENCE

收录：SCI-EXPANDED(收录号：WOS:000843903200001)、、Scopus(收录号：2-s2.0-85137019289)、WOS

基金：Funding This work was supported in part by the National Natural Science Foundation of China under Grant 42176167, and the Innovation Project Foundation of Guangdong Ocean University under Grant 18307.

语种：英文

外文关键词：rip currents; deep learning; joint dilated convolution module; multi-scale fusion; detection algorithm

外文摘要：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 x 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.