Intelligent Mineral Identification: Automated Segmentation Techniques Using Deep Learning

Mineral identification is a foundational aspect of geological research, yet traditional approaches have been severely hindered by manual analysis—characterized by inefficient workflows and prohibitive costs. This study introduces an innovative semantic segmentation methodology for automated mineral identification, leveraging advanced deep learning techniques. Our proposed model ingeniously integrates the U-Net architecture with Contextual Transformer (CoT) modules, synthesizing the precision of convolutional neural networks in local feature extraction with Transformer’s sophisticated capabilities for modeling global contextual relationships. In contrast to conventional segmentation models, it demonstrates exceptional performance in processing intricate details and boundary segmentation, offering a robust and efficient solution for precise mineral identification.