# A Fast Recognition Method for Dynamic Blasting Fragmentation Based on YOLOv8 and Binocular Vision
## 5 Conclusions  
Rapid recognition technology for dynamic blasting fragmentation is an important research topic for evaluating blasting effectiveness and improving construction efficiency. This study proposes a dynamic blasting fragmentation rapid recognition model and independently develops software for its application in engineering sites. The model dynamically captures the size distribution of surface ores loaded onto mining trucks using a binocular camera and then achieves rapid dynamic recognition of blasting fragmentation through a series of integrated algorithms based on the YOLOv8 core algorithm. This study proposes a blasting fragmentation recognition model integrating the YOLOv8 instance segmentation model, binocular vision-based automatic capture technology, fully automated annotation techniques, data augmentation, ROI extraction, and shadow data enhancement methods to improve the accuracy and robustness of dynamic evaluation of mine blasting effects and actual rock sizes. The training dataset for the model consists of 1536 diverse images from the Husab Uranium Mine in Namibia and domestic mines. The YOLOv8 instance segmentation model is responsible for detecting and classifying crushed rocks. Through data augmentation techniques, the dataset size was expanded to 7680 samples during training, enhancing the model's robustness. To accurately compute rock particle size, the [PERSON]'s algorithm was used to determine the circumcircle diameter of the rocks. By integrating the depth maps obtained through binocular vision-based automatic capture with the original images, the scale ratio between pixel size and actual size was calculated. Using this scale ratio, the actual particle size of each rock fragment was accurately extracted from the depth map, and both the depth map and actual fragment sizes were successfully obtained. Considering engineering site conditions, ROI extraction and shadow data enhancement techniques were introduced to enable the model to focus on the blasting fragmentation area, reducing background interference and improving recognition accuracy. The shadow data enhancement technique effectively simulates various lighting and shadow conditions in the mining environment, further improving the model's robustness under complex lighting conditions. This provides effective support for mine blasting evaluation and production process optimization. Finally, software and a system were independently developed based on this integrated model and successfully applied at the Husab Uranium Mine in Namibia, achieving excellent fragmentation recognition results. In summary, the proposed model was successfully implemented at the Husab Mine, and its recognition performance was verified as superior through comparative analysis with mature commercial software. The successful application of this technology provides a new technical pathway for the development of intelligent and automated mining. Future research will focus on further improving the accuracy of fragmentation recognition. Thiswill be achieved by optimizing the YOLOv8 model, integrating advanced sensors, and developing custom auxiliary algorithms to enable ultra-high-precision recognition in complex environments. These efforts will promote the development of smart mines and contribute to the realization of fully automated mining operations. Conceptualization, Y.L., M.T. and Z.X. ; methodology, Y.L. ; validation, Y.X. ; formal analysis, Z.X. ; resources, Y.X. and L.H. ; data curation, G.X. ; writing--original draft preparation, Y.L. ; writing--review and editing, M.T. ; visualization, Z.X. ; project administration, Y.L. ; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript. The research presented in this paper was funded by the National Natural Science Foundation of China (Grant Nos. 12072376). Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. Authors [PERSON] and [PERSON] were employed by the company CGNPC Uranium Resources Co., Ltd.