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Accurate estimation of fish length in single camera photogrammetry with a fiducial marker

机译:用基准标记精确估计单个相机摄影测量中的鱼长

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摘要

Videogrammetry and photogrammetry are increasingly being used in marine science for unsupervised data collection. The camera systems employed are complex, in contrast to "consumer" digital cameras and smartphones carried by potential citizen scientists. However, using consumer cameras in photogrammetry will introduce unknown length estimation errors through both the image acquisition process and lens distortion. This study presents a methodology to achieve accurate 2-dimensional (2-D) total length (TL) estimates of fish without specialist equipment or proprietary software. Photographs of fish were captured with an action camera using a background fiducial marker, a foreground fiducial marker and a laser marker. The geometric properties of the lens were modelled with OpenCV to correct image distortion. TL estimates were corrected for parallax effects using an algorithm requiring only the initial length estimate and known fish morphometric relationships. Correcting image distortion decreased RMSE by 96% and the percentage mean bias error (%MBE) by 50%. Correcting for parallax effects achieved a %MBE of -0.6%. This study demonstrates that the morphometric measurement of different species can be accurately estimated without the need for complex camera equipment, making it particularly suitable for deployment in citizen science and other volunteer-based data collection endeavours.
机译:视频测量和摄影测量越来越多地用于船舶科学中的无监督数据收集。与潜在的公民科学家携带的“消费者”数码相机和智能手机相比,所用的相机系统是复杂的。然而,在摄影测量中使用消费者摄像机将通过图像采集过程和镜头失真引入未知的长度估计误差。本研究提出了一种方法,以实现无需专业设备或专有软件的鱼类的准确二维(2-D)总长度(TL)估计。使用背景基准标记,前景基准标记和激光标记,用动作相机捕获鱼的照片。镜头的几何属性用OpenCV建模以校正图像失真。使用仅需要初始长度估计和已知的鱼类形态学关系的算法来校正T1估计。校正图像失真将RMSE减少96%,百分比偏差误差(%MBE)达到50%。校正视差效应达到-0.6%的%MBE。本研究表明,无需复杂的相机设备即可准确地估计不同物种的形态测量,使其特别适用于公民科学和其他基于志愿者的数据收集的部署。

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