评估 Timex 采样策略对海岸线探测分析精度的作用

Coasts Pub Date : 2024-05-09 DOI:10.3390/coasts4020018
Siegmund Nuyts, Eugene J. Farrell, Sheena Fennell, Stephen Nash
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引用次数: 0

摘要

利用海岸线边缘探测的远程视频图像被广泛应用于沿岸监测,以获取近岸和冲刷特征的 测量数据。其中一些系统受到设置时间长、定位要求高和硬件成本高的限制。因此,需要一种既能在现场快速部署,又能产生沿岸监测所需的结果的低成本自 主系统(约 500 欧元),如 Timex 摄像机。这项研究评估了取样策略(延时间隔)对位于爱尔兰西部偏远沿海地区、俯瞰耗散海滩-沙丘系统的两台低成本照相机的海岸线探测精度的影响。分析表明,采样间隔在 1 秒到 30 秒之间时,检测到的海岸线 RMSD 与其他研究结果相似(即 1 号相机的 RMSDmean = 1.4 米,2 号相机的 RMSDmean = 0.9 米),采样间隔从 1 秒增加到 30 秒对海岸线检测精度没有明显不利影响。研究表明,根据检测到的海岸线的预期用途,在使用 Timex 摄像机时,可以将目前 1 秒的图像采样间隔标准延长到 30 秒,而不会明显降低精度。这对电池寿命和内存存储产生了积极影响,使系统更具自主性;例如,当采样间隔从 1 秒增加到 5 秒时,电池寿命从约 10 天增加到约 100 天。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Assessment of the Role of the Timex Sampling Strategy on the Precision of Shoreline Detection Analysis
Remote video imagery using shoreline edge detection is widely used in coastal monitoring in order to acquire measurements of nearshore and swash features. Some of these systems are constrained by their long setup time, positioning requirements and considerable hardware costs. As such, there is a need for an autonomous low-cost system (~EUR 500), such as Timex cameras, that can be rapidly deployed in the field, while still producing the outcomes required for coastal monitoring. This research presents an assessment of the effect of the sampling strategy (time-lapse intervals) on the precision of shoreline detection for two low-cost cameras located in a remote coastal area in western Ireland, overlooking a dissipative beach–dune system. The analysis shows that RMSD in the detected shoreline is similar to other studies for sampling intervals ranging between 1 s and 30 s (i.e., RMSDmean for Camera 1 = 1.4 m and Camera 2 = 0.9 m), and an increase in the sampling interval from 1 s to 30 s had no significant adverse effect on the precision of shoreline detection. The research shows that depending on the intended use of the detected shorelines, the current standard of 1 s image sampling interval when using Timex cameras can be increased up to 30 s without any significant loss of accuracy. This positively impacts battery life and memory storage, making the systems more autonomous; for example, the battery life increased from ~10 days to ~100 days when the sampling interval was increased from 1 to 5 s.
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