Ore Image

Ore image analysis focuses on automatically extracting valuable information from images of ore samples, primarily for optimizing mining processes like beneficiation and grade control. Current research emphasizes developing efficient and accurate segmentation methods using lightweight neural network architectures such as Multi-Layer Perceptrons (MLPs) and Ghost-FPN, often incorporating texture features and novel loss functions to address challenges like ambiguous boundaries and limited labeled data. These advancements enable faster and more accurate particle size distribution analysis, improved few-shot object detection, and ultimately, more efficient and profitable mining operations.