Rashid Ashraf Haroon, Razzak Imran, Tanveer M, Hobbs Michael
Department of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India; School of Computer Science and Engineering, University of New South Wales, Australia; School of Information Technology, Deakin University, Geelong, Australia.
Department of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India; School of Computer Science and Engineering, University of New South Wales, Australia; School of Information Technology, Deakin University, Geelong, Australia.
ISA Trans. 2023 Jan;132:199-207. doi: 10.1016/j.isatra.2022.05.015. Epub 2022 May 26.
Rip Currents are contributing around 25 fatal drownings each year in Australia. Previous research has indicated that most of beachgoers cannot correctly identify a rip current, leaving them at risk of experiencing a drowning incident. Automated detection of rip currents could help to reduce drownings and assist lifeguards in supervision planning; however, varying beach conditions have made this challenging. This work presents the effectiveness of an improved lightweight framework for detecting rip currents: RipDet+, aided with residual mapping to boost the generalization performance. We have used Yolo-V3 architecture to build RipDet framework and utilize pretrained weight by fully exploiting the detection training set from some base classes which in result quickly adapt the detection prediction to the available rip data. Extensive experiments are reported which show the effectiveness of RipDet+ architecture in achieving a detection accuracy of 98.55%, which is significantly greater compared to other state-of-the-art methods for Rip currents detection.
在澳大利亚，离岸流每年导致约25人溺水死亡。先前的研究表明，大多数海滩游客无法正确识别离岸流，这使他们面临溺水事故的风险。离岸流的自动检测有助于减少溺水事件，并协助救生员进行监管规划；然而，不同的海滩条件使这具有挑战性。这项工作展示了一种改进的轻量级离岸流检测框架RipDet+的有效性，该框架借助残差映射来提高泛化性能。我们使用Yolo-V3架构构建RipDet框架，并通过充分利用来自一些基础类别的检测训练集来使用预训练权重，从而使检测预测能够快速适应可用的离岸流数据。报告了大量实验，这些实验表明RipDet+架构在实现98.55%的检测准确率方面的有效性，与其他用于离岸流检测的最先进方法相比，这一准确率显著更高。
