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High-Speed Weight Estimation of Whole Herring (Clupea harengus) Using 3D Machine Vision

机译:使用3D机器视觉对整个鲱鱼(Clupea harengus)进行高速重量估计

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摘要

Weight is an important parameter by which the price of whole herring (Clupea harengus) is determined. Current mechanical weight graders are capable of a high throughput but have a relatively low accuracy. For this reason, there is a need for a more accurate high-speed weight estimation of whole herring. A 3-dimensional (3D) machine vision system was developed for high-speed weight estimation of whole herring. The system uses a 3D laser triangulation system above a conveyor belt moving at a speed of 1000 mm/s. Weight prediction models were developed for several feature sets, and a linear regression model using several 2-dimensional (2D) and 3D features enabled more accurate weight estimation than using 3D volume only. Using the combined 2D and 3D features, the root mean square error of cross-validation was 5.6 g, and the worst-case prediction error, evaluated by cross-validation, was ±14 g, for a sample (« = 179) of fresh whole herring. The proposed system has the potential to enable high-speed and accurate weight estimation of whole herring in the processing plants.
机译:重量是确定整个鲱鱼价格的重要参数。当前的机械重量分级机能够具有高产量,但是具有相对较低的精度。因此,需要对整个鲱鱼进行更精确的高速重量估计。开发了3维(3D)机器视觉系统,用于整个鲱鱼的高速重量估计。该系统在传送带上方以1000 mm / s的速度使用3D激光三角测量系统。权重预测模型是为多个特征集开发的,与仅使用3D体积相比,使用多个2维(2D)和3D特征的线性回归模型可以实现更精确的权重估计。使用2D和3D组合特征,交叉验证的均方根误差为5.6 g,通过交叉验证评估的最新鲜样本(«= 179)的最坏情况预测误差为±14 g整个鲱鱼。所提出的系统具有实现加工厂中整个鲱鱼的高速准确重量估计的潜力。

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