Intelligent Lithology Identification Methods for Rock Images Based on Object Detection

Lithology identification is a crucial step in geological research. In recent years, the development of artificial intelligence technologies has provided new insights into solving problems associated with subjectivity and labor intensity of traditional manual identification. However, when rocks are identified in situ, existing algorithms cannot accurately identify them if the image features of different types of rocks are similar or the rock textures are varied. In this regard, the study of lithology identification for the rock images captured from the field was carried out. First, the object detection algorithm of single shot multibox detector was improved by adding residual net and adaptive moment estimation, and a lithology identification model was constructed. Second, based on the above improved algorithm, the technologies of database and geographic information system were combined to develop an integrated identification method. Third, the proposed methods were applied to 12 types of rocks in Xingcheng area, China, for testing their validity, and feasibility in field geological surveys. Finally, the effects of learning rate and batch size on the identification were discussed, as the epoch number was increased. We found that the average accuracies of the improved single shot multibox detector and integrated method were 89.4% and 98.4%, respectively. The maximum accuracy could even reach 100%. The identification results were evaluated based on accuracy, precision, recall, F1-score, and mean average precision. It was demonstrated that the integrated method has a strong identification ability compared with other neural network methods. Generally, a small learning rate can lead to low loss and high accuracy, whereas a small batch size can lead to high loss and high accuracy. Moreover, the newly proposed methods helped to improve the lithology identification accuracy in the field and support the study of intelligent in situ identification for rock images.

Automated summary

This study focuses on the lithology identification of rock images captured from the field. An improved object detection algorithm and an integrated identification method were proposed. The results showed that the accuracy of the improved algorithm was 89.4%, while the integrated method achieved an accuracy of 98.4% and even reached 100% in some cases. The newly proposed methods helped to improve the lithology identification accuracy in the field and supported intelligent in situ identification for rock images.