Research on Intelligent and High-Precision Structure-Recognition Methods for Field Geological Outcrop Images

Abstract

The accurate recognition of geological structures in field outcrop images is critical for applications such as geological hazard analysis, seismic risk assessment, and urban geological planning. However, traditional manual interpretation of geological images is time-consuming, labor-intensive, and subjective, limiting its scalability and precision. To address this gap, this study proposes an intelligent, automated recognition method for field geological outcrop images based on deep learning techniques. The methodology integrates Fourier transform, Canny edge detection, and Mask R-CNN instance segmentation, enhanced with image normalization and data augmentation strategies such as grayscale conversion, Gaussian filtering, and rotation. A custom dataset comprising 4260 images was constructed and annotated using a hybrid approach involving edge detection and expert labeling. The proposed model, improved with PrRoI Pooling, outperforms conventional models such as YOLOv3, Faster R-CNN, and standard Mask R-CNN, achieving a mean average precision (mAP) of 90.77% in detecting fault, fold, and sausage-like geological structures. The results demonstrate the model's robustness, accuracy, and suitability for complex geological environments. This study not only advances the state-of-the-art in geological image recognition but also lays a foundation for future research into broader structural classification, multi-modal geological data integration, and real-time field deployment.