YOLO-Rip: A modified lightweight network for Rip currents detection
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 cur...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2022.930478/full |
| _version_ | 1798040408062689280 |
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| author | Daoheng Zhu Rui Qi Pengpeng Hu Qianxin Su Xue Qin Zhiqiang Li |
| author_facet | Daoheng Zhu Rui Qi Pengpeng Hu Qianxin Su Xue Qin Zhiqiang Li |
| author_sort | Daoheng Zhu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:07:12Z |
| format | Article |
| id | doaj.art-864adbae7d2e462581eb30911e37ff8a |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-04-11T22:07:12Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-864adbae7d2e462581eb30911e37ff8a2022-12-22T04:00:41ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452022-08-01910.3389/fmars.2022.930478930478YOLO-Rip: A modified lightweight network for Rip currents detectionDaoheng Zhu0Rui Qi1Pengpeng Hu2Qianxin Su3Xue Qin4Zhiqiang Li5School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, ChinaSchool of Big Data and Information Engineering, Guizhou University, Guiyang, ChinaSchool of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, ChinaSchool of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, ChinaSchool of Big Data and Information Engineering, Guizhou University, Guiyang, ChinaSchool of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, ChinaRip 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.https://www.frontiersin.org/articles/10.3389/fmars.2022.930478/fullrip currentsdeep learningjoint dilated convolution modulemulti-scale fusiondetection algorithm |
| spellingShingle | Daoheng Zhu Rui Qi Pengpeng Hu Qianxin Su Xue Qin Zhiqiang Li YOLO-Rip: A modified lightweight network for Rip currents detection Frontiers in Marine Science rip currents deep learning joint dilated convolution module multi-scale fusion detection algorithm |
| title | YOLO-Rip: A modified lightweight network for Rip currents detection |
| title_full | YOLO-Rip: A modified lightweight network for Rip currents detection |
| title_fullStr | YOLO-Rip: A modified lightweight network for Rip currents detection |
| title_full_unstemmed | YOLO-Rip: A modified lightweight network for Rip currents detection |
| title_short | YOLO-Rip: A modified lightweight network for Rip currents detection |
| title_sort | yolo rip a modified lightweight network for rip currents detection |
| topic | rip currents deep learning joint dilated convolution module multi-scale fusion detection algorithm |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2022.930478/full |
| work_keys_str_mv | AT daohengzhu yoloripamodifiedlightweightnetworkforripcurrentsdetection AT ruiqi yoloripamodifiedlightweightnetworkforripcurrentsdetection AT pengpenghu yoloripamodifiedlightweightnetworkforripcurrentsdetection AT qianxinsu yoloripamodifiedlightweightnetworkforripcurrentsdetection AT xueqin yoloripamodifiedlightweightnetworkforripcurrentsdetection AT zhiqiangli yoloripamodifiedlightweightnetworkforripcurrentsdetection |