A deep learning-based method for the intelligent identification of the quantity of coals flushed out during borehole hydraulic flushing - Xiaojun LI; Mingyang ZHAO; Miao LI (Meitian dizhi yu kantan)
ObjectiveGiven the inaccurate and low-efficiency manual statistics of the quantity of coals flushed out during borehole hydraulic flushing, this study proposed an intelligent identification method that combines YOLOv8n, ResNet34, and PP-OCRv4 algorithms. MethodsFirst, the first-level detection was completed using the YOLOv8n algorithm, which, combined with the ResNet34 and PP-OCRv4 algorithms via parallel cascade, was then employed to conduct second-level processing. Through the above processing and in combination with the method for classification status discrimination based on tracking frame numbers, the framework of an algorithm for automatically calculating the quantity of coals flushed out was established. Subsequently, the deformable convolution DCNv2 module was introduced into the C2f module of YOLOv8n to reduce the impacts of point-like strong illumination on feature extraction. Moreover, the default detection head of YOLOv8n was replaced with the Dynamic Head module to strengthen the feature extraction in scale, space, and channel dimensions. The CIoU loss function was replaced with the SIoU loss function to accelerate the matching between prediction and ground truth boxes. Finally, the improved YOLOv8n algorithm was validated using a self-built dataset. Results and Conclusions The results indicate that, compared to the original algorithm, the improved YOLOv8n algorithm increased the mean average classification accuracy, recall, and precision by 7.6%, 3.5%, and 6.4%, respectively. This verifies the effectiveness and stability of the improvement strategy for enhancing the model performance. The improved YOLOv8n algorithm was applied to the real-time videos from four drilling sites of borehole hydraulic flushing for gas drainage, yielding respective identification accuracies of 100.0%, 93.3%, 95.7%, and 93.1%, with an average of 95.5%, meeting the accuracy requirements for the automatic identification of coal quantity flushed out during borehole hydraulic flushing. The method for determining the ResNet34 classification status based on tracking frame numbers resolved the problem of unreliable single identification of the classification status. The results of this study provide a technical and practical foundation for the integration of the YOLO series of algorithms with other deep learning techniques and its wide applications. Besides, these results serve as a valuable reference for achieving intelligent advances in complex underground coal mine scenarios such as drilling sites for gas drainage.