In the quality control of cigarette production, achieving precise detection of four types of tobacco shred (tobacco silk, cut stem, expanded tobacco silk, reconstituted tobacco shred) blending ratios has emerged as a critical technical challenge. To address the detection difficulties arising from subtle morphological variations and prevalent overlapped distributions of tobacco shreds, this study proposes a rapid overlapped tobacco shred segmentation algorithm based on an enhanced YOLOv8 framework. The method reconstructs the backbone network using a Res2Net architecture to amplify feature extraction capabilities for minute and complex patterns, while integrating ContextGuidedBlock (CGB) modules into the neck network to enhance boundary recognition accuracy in overlapped regions. Experimental results demonstrate that the improved model achieves notable performance metrics of mAP50 (86.5%), mAP50-95 (67.8%), and recall rate (81.9%) while maintaining real-time processing speed at 67 fps. Through ablation studies and comparative analyses with mainstream segmentation networks, the effectiveness and performance advantages of the proposed architectural modifications are rigorously validated. This algorithm not only improves segmentation precision but also optimizes frame rates, demonstrating superior applicability in practical production line environments.