Cobalt, as a rare metal, plays an important role in strategic resources due to its physical, chemical, and mechanical properties in applications. In order to achieve automatic control of the cobalt extraction process, it is necessary to conduct online detection of the color characteristics of the cobalt extraction solution. Unfortunately, due to the lack of online automatic detection methods for the color of cobalt extraction solution, the automatic control of the extraction process has not yet been achieved. Currently, the color of cobalt extraction solution needs to be observed and judged by operators based on experience. This will result in significant detection errors and time lag. To change this situation, an online detection method for color features of cobalt extraction solution based on machine vision is proposed in this article. Firstly, we establish a real-time image acquisition environment for detecting object colors, and then use the Gaussian filters and K-means algorithm to denoise and segment the image. In order to correct color distortion in images caused by ambient light, we measure the color feature values of a set of standard color cards using the machine vision system and a standard spectrophotometer. Based on this test data, we establish chromaticity correction models for the extraction solution using back propagation (BP) network and radial basis function (RBF) network, respectively. The models can correct the color characteristic values of the extraction solution, overcome environmental interference, and thus obtain more accurate color characteristic values of the extraction solution. The established model can be used for online automatic color detection of cobalt extraction solution. The modeling results indicate that in the established color measurement environment, the RBF network model outperforms the BP network model in color feature detection accuracy.

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基于机器视觉的钴提取液颜色特征在线检测方法

钴作为一种稀有金属，由于其物理、化学和机械性能在应用中发挥着重要的战略资源作用。为了实现钴提取过程的自动控制，需要对钴提取液的颜色特征进行在线检测。遗憾的是，由于缺乏钴提取液颜色的在线自动检测方法，尚未实现提取过程的自动控制。目前，钴提取液的颜色需要操作人员根据经验观察判断。这将导致严重的检测错误和时间滞后。为了改变这一现状，本文提出一种基于机器视觉的钴提取液颜色特征在线检测方法。首先建立实时图像采集环境来检测物体颜色，然后使用高斯滤波器和K-means算法对图像进行去噪和分割。为了校正环境光引起的图像颜色失真，我们使用机器视觉系统和标准分光光度计测量一组标准色卡的颜色特征值。基于此测试数据，我们分别使用反向传播（BP）网络和径向基函数（RBF）网络建立提取解的色度校正模型。该模型可以对提取液的颜色特征值进行修正，克服环境干扰，从而获得更准确的提取液颜色特征值。建立的模型可用于钴提取液颜色的在线自动检测。建模结果表明，在建立的颜色测量环境下，RBF网络模型在颜色特征检测精度上优于BP网络模型。