# A Fast Recognition Method for Dynamic Blasting Fragmentation Based on YOLOv8 and Binocular Vision
## 1 Introduction  
Blasting is the most efficient and commonly used method for mineral resource extraction, especially in open-pit mining [1]. The rock fragmentation size after blasting is a key indicator for evaluating blasting performance and provides a basis for designing blasting schemes [2, 3]. The distribution of rock fragment size after blasting significantly affects subsequent mineral processing [4]. Optimizing the blasting design to reduce the energy consumption in the subsequent crushing and grinding stage is an important topic in the blasting field, making accurate identification of post-blast rock size increasingly critical [5, 6]. Traditional methods for identifying rock fragment size include manual sieving, laser scanning, and image recognition based on computer vision [7, 8]. Manual sieving is labor-intensive and inefficient, while laser scanning effectively addresses these issues but is relatively costly [9]. Compared to laser scanning, image recognition technology offers lower accuracy but significantly reduced costs [10]. With the rapid development of computervision and deep learning algorithms, image-based recognition techniques have been widely adopted in rock fragmentation studies, improving the accuracy limitations of traditional methods [11]. Current blast fragmentation recognition mainly focuses on achieving mask segmentation of rocks and obtaining their actual sizes [12]. [PERSON] et al. [13] encoded images using CNN and enhanced the segmentation of digital rock images with a convolutional autoencoder algorithm. Experimental results showed that compared with traditional multi-threshold segmentation, the segmentation results obtained by this method were more accurate and reliable. To compare the performance of different CNN models in rock segmentation, [PERSON] et al. [14] studied the application of three popular CNN architectures in the segmentation of 3D micro-tomography images of various rock samples and found that the trained models exhibited high generalization capabilities. [PERSON] et al. [15] proposed an image segmentation method, UR, based on deep learning U-Net and Res Unet networks. A comparison with existing image segmentation methods demonstrated the efficiency and accuracy of the newly proposed UR method. [PERSON] et al. [16] proposed a complex ore image segmentation algorithm based on template transformation and reconstruction, demonstrating the accuracy of the proposed algorithm in segmenting complex images. The above study achieved accurate segmentation of rock fragment masks. These segmentation models typically rely on high-quality data and require high computational costs. However, in scenarios such as ore transportation, which require object recognition, localization, and rapid response, the single forward propagation and lightweight architecture of YOLOv8 are better suited to the mining environment. Therefore, YOLOv8 was selected as the primary model for mask segmentation in this study. Rock particle size is typically measured either manually or with the aid of reference objects. Manual measurement is inefficient and labor-intensive. Although reference-assisted methods improve efficiency and reduce costs, they sacrifice accuracy and therefore fail to fundamentally resolve the issue. Camera vision measurement utilizes machine vision principles to capture object dimensions, offering a solution to traditional recognition problems [17]. The current mainstream camera-based visual measurement methods are divided into monocular and binocular vision. Monocular vision requires the use of a reference object to obtain the actual size of rock fragments and demands that the camera and target remain relatively stationary. Currently, most fragmentation recognition methods use monocular cameras to capture multiple images of blast piles with reference objects for analysis. This approach cannot achieve intelligent fragmentation recognition [18; 19]. Therefore, this study adopts binocular vision to calculate post-blast rock particle size.