YOLO-Rip: A modified lightweight network for Rip currents detection
نویسندگان
چکیده
Rip currents form on beaches worldwide and pose a potential safety hazard for beach visitors. Therefore, effectively identifying rip from scenes providing real-time alerts to managers beachgoers is crucial. In this study, the YOLO-Rip model was proposed detect current targets based popular deep learning techniques. First, characteristics of large target size in images, neck region YOLOv5s streamlined. The 80 × feature map branches suitable detecting small were removed reduce number parameters, decrease complexity model, improve detection performance. Subsequently, we adding joint dilated convolutional (JDC) module lateral connection pyramid network (FPN) expand perceptual field, information utilization, while keeping compact. Finally, SimAM module, which parametric-free attention mechanism, added optimize accuracy. Several mainstream neural models have been used train self-built image datasets. experimental results show that (i) different using same dataset vary greatly (ii) compared with YOLOv5s, increased mAP value by approximately 4% (to 92.15%), frame rate 2.18 frames per second, only 0.46 MB. modified improved accuracy streamlined, indicating its efficiency currents.
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ژورنال
عنوان ژورنال: Frontiers in Marine Science
سال: 2022
ISSN: ['2296-7745']
DOI: https://doi.org/10.3389/fmars.2022.930478