As the primary method used in open-pit mining, blasting has a direct impact on the efficiency and cost of subsequent operations. Therefore, dynamic identification of rock fragment size after blasting is essential for evaluating blasting quality and optimizing mining plans. This study presents a YOLOv8-based binocular vision model for real-time recognition of blasting fragmentation. The model is trained on a dataset comprising 1536 samples, which were annotated using an automatic labeling algorithm and expanded to 7680 samples through data augmentation techniques. The YOLOv8 instance segmentation model is employed to detect and classify rock fragments. By integrating binocular vision-based automatic image capture with Welzl’s algorithm, the actual particle size of each rock fragment is calculated. Furthermore, region of interest (ROI) extraction and shadow-based data enhancement techniques are incorporated to focus the model on the blasting fragmentation area and reduce environmental interference. Finally, software and a system were independently developed based on this integrated model and successfully deployed at engineering sites. The dynamic recognition Mean Average Precision of this integrated model is 0.84, providing a valuable reference for evaluating blasting effects and improving work efficiency.