Application of deep learning-based object segmentation in intelligent rock recognition

<p indent="0mm">With the rapid development of artificial intelligence technologies, significant advancements have been made for the application of deep learning in the field of image processing, particularly in the field of object detection and segmentation. Traditional rock recognition methods, limited by complex backgrounds and the diversity of rock types, can not meet the practical demand. The rapid growth of deep learning offers new approaches and technical supports for the intelligent rock recognition. This study aims to apply the deep learning model YOLOv8-seg into the intelligent rock recognition tasks, in order to evaluate the performance and stability of deep learning in object detection and segmentation, and to provide technical support for geological exploration and management of mineral resources. The YOLOv8-seg model was applied for the training to recognize various rock types including basalt, granite, marble, quartzite, coal, limestone, and sandstone, in order to optimize the intelligent rock recognition capability of the model. The model combines object detection and instance segmentation functions, and uses multiple loss functions, such as box_loss, seg_loss, cls_loss, and dfl_loss, to optimize the bounding box prediction, segmentation performance, class recognition accuracy, and regression precision. For the object segmentation tasks by using the YOLOv8-seg model, precision(B) and recall(B) reached 0.91284 and 0.93587, mAP50(B) and mAP50-95(B) reached 0.86666 and 0.83686, precision(M) and recall(M) reached 0.90394 and 0.93438, mAP50(M) and mAP50-95(M) reached 0.85931 and 0.81856, respectively, indicating that high segmentation accuracies and recall rates can be achieved by applying the model. The F1 Score(B) and F1Score(M) respectively reached 0.92421 and 0.91891 in Round 551 of the training, showing significant improvements from their initial values. The results of the test set indicate that all average confidence levels of over 90% have been reached for the intelligent recognition of rock types such as basalt, coal, and limestone by applying the model. All average recognition rates of over 85% have been reached for the practical application scenarios like rock mining and coal transportation by using the model. Excellent performances, including high precision, recall, and stability, have been demonstrated in intelligent rock recognition tasks by applying the YOLOv8-seg model. Thus, the YOLOv8-seg model is suitable for completing various tasks of the classification and recognition of rocks. The research findings suggest that the model has broad potential of applications in geological exploration and management of mineral resources, and offers a reliable solution for intelligent rock recognition.