This study focuses on the parameters necessary for the successful deployment of intelligent control systems for flotation processes, focusing on froth surface characteristics. Depth information is not available for conventional 2D features extracted from single froth images, thereby constraining their capacity to fully capture froth surface characteristics. To overcome this limitation, this study proposes a methodology for extracting 3D froth features by employing a deep learning-based binocular stereo vision approach. Initially, the froth image is preprocessed, including correction and segmentation. Subsequently, a froth stereo matching model called dual-attention encoding volume stereo (DAEV-Stereo) is developed. This model is trained utilizing simulated and real-world datasets to determine the froth parallax. The distance between the froth surface and camera is computed through the binocular vision approach based on camera intrinsics and extrinsics. Subsequently, the Froth is reconstructed in three dimensions, and the surface area and volume are calculated utilizing the triangular prism differential traversal technique. A mathematical model is formulated to integrate depth information and froth layer thickness to compute colour characteristics. The experimental results indicate that the DAEV-Stereo model obtains an endpoint error of 0.5 for froth stereo matching and a processing speed of 0.37 s, thereby satisfying the operational criteria for industrial use. After conducting pilot plant flotation tests, the median absolute deviations for the froth surface area, volume, and colour features were determined as 18.42, 17.62, and 9.67, respectively. Moreover, compared with traditional features, stereo features demonstrate reduced fluctuations, improved stability, and better correlations with operational conditions. The binocular stereo vision approach and DAEV-Stereo model for extracting 3D froth features are valuable for accurately characterizing froth surface data.

中文翻译：

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基于双注意力编码体积立体匹配模型和双目立体视觉提取的浮选泡沫3D特征表征


本研究重点关注成功部署浮选过程智能控制系统所需的参数，重点关注泡沫表面特性。从单个泡沫图像中提取的传统二维特征无法获得深度信息，从而限制了它们完全捕获泡沫表面特征的能力。为了克服这一限制，本研究提出了一种通过采用基于深度学习的双目立体视觉方法来提取 3D 泡沫特征的方法。首先，对泡沫图像进行预处理，包括校正和分割。随后，开发了一种称为双注意编码体积立体声（DAEV-Stereo）的泡沫立体声匹配模型。该模型利用模拟和真实数据集进行训练以确定泡沫视差。泡沫表面和相机之间的距离是通过基于相机内参和外参的双目视觉方法计算的。随后，在三维空间中重建泡沫，并利用三棱柱微分遍历技术计算表面积和体积。制定数学模型来整合深度信息和泡沫层厚度来计算颜色特征。实验结果表明，DAEV-Stereo模型的泡沫立体匹配端点误差为0.5，处理速度为0.37 s，满足工业应用的运行标准。进行中试工厂浮选试验后，泡沫表面积、体积和颜色特征的中值绝对偏差分别确定为 18.42、17.62 和 9.67。 此外，与传统特征相比，立体特征表现出波动减少、稳定性提高、与操作条件的相关性更好。用于提取 3D 泡沫特征的双目立体视觉方法和 DAEV-Stereo 模型对于准确表征泡沫表面数据非常有价值。