Lithology Identification Based on Improved Faster R-CNN

In the mining industry, lithological identification is crucial for ensuring the safety of equipment and personnel, as well as for improving production efficiency. Traditional ore identification methods, such as visual inspection, physical testing, and chemical analysis, have many limitations in terms of their operational complexity and applicability. Modern ore identification technologies, especially those combined with deep learning methods, can effectively overcome these shortcomings and significantly enhance identification performance. However, mainstream deep learning object detection algorithms still face the issues of low accuracy and poor identification performance in challenging mining conditions. To handle these problems, an improved Faster R-CNN model is proposed in this study. Specifically, we replace the backbone network ResNet with Res2Net-50 and incorporate an improved Feature Pyramid Network (FPN) to enhance feature fusion, thereby further improving the model’s feature extraction capability. Region of Interest(ROI) Align replaces the ROI pooling layer to solve the spatial misalignment issue, providing a higher detection accuracy in tasks involving small object detection and precise boundary detection. Additionally, the backbone feature extraction network integrates an efficient channel attention (ECA) module to optimize high-resolution semantic information maps. By adding simulated noise, the model’s robustness and anti-interference capabilities are enhanced. Soft-NMS is used instead of traditional NMS, preserving more potential targets through a confidence decay mechanism, thereby improving the detection accuracy and robustness. The experimental results show that the improved Faster R-CNN model maintains efficient and accurate ore identification capabilities even in complex mining environments, demonstrating its great potential in practical applications. The model achieves significant improvements in detection accuracy and efficiency, providing strong support for the intelligent and automated identification of ores.