Evaluation of Classical Descriptors coupled to Support Vector Machine Classifier for Phosphate ore Screening monitoring

Abstract

Phosphorus is an important and finite resource that is utilized mainly to produce phosphate fertilizers that assist in crop production. From phosphate ore to phosphate a process of beneficiation is required to remove the unnecessary minerals contains in the phosphate ore and to increase the grade concentration of mining product. The screening unit is a very important and critical step in this process. However, during this stage, many dysfunctions and anomalies can occur which impact the yield and quality of the product. Hence, it is essential to be monitored for real-time quality control. The purpose of this work is to automate surveillance and anomaly detection on the screening unit by using artificial vision techniques. Classical and supervised image classification approach has been used based on tree manual descriptors; HOG, SIFT, and LBP combined each with the support vector machine classifier. The evaluation of the three combinations shows that the HOG-SVM combination has the best trade-off between both accuracy and runtime.