# A Fast Recognition Method for Dynamic Blasting Fragmentation Based on YOLOv8 and Binocular Vision
## 2 Materials and Methods
### YOLOv8 Instance Segmentation Model  
In engineering practice, it has been found that fragmentation recognition is significantly affected by factors such as differences in rock mass size, stacking coverage, complex surface texture features, and the similarity in grayscale between the background and target. Even with the use of traditional threshold-based segmentation algorithms for high-resolution blast images, issues such as blurred boundary recognition and region adhesion still exist, leading to accuracy defects. Therefore, exploring intelligent image analysis methods that balance computation speed and segmentation accuracy to achieve dual optimization of processing efficiency and precision is of significant engineering value for enhancing dynamic monitoring of blast fragmentation and optimizing blast process decisions.  
This study uses the YOLOv8 instance segmentation model for rock fragment segmentation. YOLO (You Only Look Once) is a real-time object detection algorithm initially proposed by [PERSON] et al. [25]. The algorithm has high detection speed and accuracy, making it suitable for various computer vision applications [26]. Unlike traditional region-based vision techniques, this method transforms the object detection task into an \"end-to-end\" regression problem. Its key feature is that it performs object detection and classification in a single forward pass, producing bounding boxes and confidence scores for all targets, achieving high detection efficiency. YOLO, as one of the mainstream networks for single-stage real-time object detection tasks, has continually improved in instance segmentation performance through updates from YOLOv1 to YOLOv8.  
As shown in Figure 6, the YOLOv8 model is divided into versions such as n, s, m, and x based on different depth and exploration coefficients. Its main structure consists of the input, feature extraction backbone, feature fusion neck network, and head network. The input stage involves reassembling rock images, mosaic data augmentation, grayscale filling, and anchor point calculation to enhance the model's robustness to different rock images. The feature extraction backbone introduces the gradient-rich C2f module, replacing the previous C3 module. Compared with the C3 module, the C2f module implements  
Figure 5: [PERSON]’s smallest enclosing circle algorithm process.  
multi-branch cross-layer connections, allowing the network to learn more features, but it is more complex in structure and has a larger number of parameters. The neck network still uses SPPCSPC, CBS, Ups, and E-ELAN modules, enhancing the feature fusion capability of the network through upsampling and downsampling. It is responsible for feature fusion of extracted rock fragment foreground target information. This enhances the receptive field while ensuring the model's detection performance and convergence speed. The head network is replaced with the mainstream decoupled head structure, which separates the classification and detection heads, each responsible for returning the classification and bounding boxes of detected objects in rock fragment images. This improves the model's accuracy in bounding box prediction, enabling object prediction of rock fragments in images.