1. An image can encode date, time, location and camera information as metadata and implicitly encodes species information and data on human activity, for example the size distribution of fish removals. Accurate length estimates can be made from images using a fiducial marker; however, their manual extraction is time‐consuming and estimates are inaccurate without control over the imaging system. This article presents a methodology which uses machine vision to estimate the total length (TL) of a fusiform fish (European sea bass).
2. Three regional convolutional neural networks (R‐CNN) were trained from public images. Images of European sea bass were captured with a fiducial marker with three non‐specialist cameras. Images were undistorted using the intrinsic lens properties calculated for the camera in OpenCV; then TL was estimated using machine vision (MV) to detect both marker and subject. MV performance was evaluated for the three R‐CNNs under downsampling and rotation of the captured images.
3. Each R‐CNN accurately predicted the location of fish in test images (mean intersection over union, 93%) and estimates of TL were accurate, with percent mean bias error (%MBE [95% CIs]) = 2.2% [2.0, 2.4]). Detections were robust to horizontal flipping and downsampling. TL estimates at absolute image rotations >20° became increasingly inaccurate but %MBE [95% CIs] was reduced to −0.1% [−0.2, 0.1] using machine learning to remove outliers and model bias.
4. Machine vision can classify and derive measurements of species from images without specialist equipment. It is anticipated that ecological researchers and managers will make increasing use of MV where image data are collected (e.g. in remote electronic monitoring, virtual observations, wildlife surveys and morphometrics) and MV will be of particular utility where large volumes of image data are gathered.

中文翻译：

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使用机器视觉通过区域卷积神经网络从图像中估计鱼的长度

1. 图像可以将日期，时间，位置和照相机信息编码为元数据，并隐式编码物种信息和有关人类活动的数据，例如鱼类清除的大小分布。可以使用基准标记从图像进行准确的长度估计；但是，如果不对成像系统进行控制，则它们的手动提取非常耗时，并且估算不准确。本文介绍了一种方法，该方法使用机器视觉来估计梭形鱼（欧洲鲈鱼）的总长度（TL）。
2. 从公共图像中训练了三个区域卷积神经网络（R-CNN）。使用三个非专业相机的基准标记来捕获欧洲鲈鱼的图像。使用在OpenCV中为相机计算的固有镜头属性，图像没有失真；然后使用机器视觉（MV）估算TL，以检测标记物和受试者。在下采样和捕获图像旋转的情况下，对三个R-CNN的MV性能进行了评估。
3. 每个R-CNN都可以准确预测鱼在测试图像中的位置（联合上的平均交点，93％），而TL的估算是准确的，平均偏差误差百分比（％MBE [95％CIs]）= 2.2％[2.0，2.4 ]）。检测对水平翻转和下采样具有鲁棒性。使用机器学习消除异常值和模型偏差时，绝对图像旋转> 20°时的TL估计变得越来越不准确，但％MBE [95％CIs]降低至-0.1％[-0.2，0.1]。
4. 机器视觉无需专业设备即可对图像进行分类并得出物种的测量结果。预计生态研究人员和管理人员将在收集图像数据的地方（例如在远程电子监测，虚拟观测，野生动植物调查和形态计量学中）更多地使用MV，并且在收集大量图像数据的情况下，MV将特别有用。