Resumen
Insufficient color feature extraction can lead to poor prediction performance in rare earth element composition estimation. To address this issue, we propose a one-dimensional convolutional method for predicting rare earth element composition. First, images of rare earth element solutions, color space features (HSV and YUV), and spatial texture features are extracted. Because the trend of rare earth element composition is closely related to the extraction stage, we select the corresponding extraction stage of the image as a key feature. A feature selection technique based on Random Forest Recursive Feature Elimination with Cross-Validation (RF-RFECV) is applied to select the most relevant features, with a mixed feature set being obtained. Based on this, a one-dimensional convolutional neural network prediction model with multiple residual attention blocks (MRAB-DNN) is introduced. The proposed model incorporates the Residual Attention Block (RAB) structure, which mitigates the effects of noisy weights, subsequently enhancing both prediction accuracy and the rate of convergence. Experimental assessments on field images utilizing the MRAB-DNN model with an amalgamation of features indicate that our methodology surpasses alternative techniques in thorough image feature extraction. Moreover, it presents dual advantages of speed and precision in predicting the composition of rare earth elements. Such a model holds potential for real-time monitoring of rare earth element composition in extraction production processes.