Real-Time Recognition and Feature Extraction of Stratum Images Based on Deep Learning

Abstract

Accurate identification and feature extraction of stratum images play a crucial role in geological exploration, resource prospecting, and mining operations. Traditional methods of stratum image identification largely rely on human experience and manual operations, which are inefficient and prone to errors. In recent years, deep learning technology has provided new methods for the identification and feature extraction of stratum images, but existing deep learning models still face challenges in computational efficiency, multi-scale feature extraction, and uneven sample distribution. This paper proposes a stratum image feature extraction network based on the pyramid model and constructs a lightweight stratum identification model for real-time recognition. By introducing a classification-regression network structure and anchor-based sample supervision rules, this study aims to improve the accuracy and efficiency of the model, providing an effective solution for real-time recognition of stratum images.