Accurate and automated detection of dental anatomy is essential for diagnosis and treatment planning. This study proposes a Transformer-Embedded YOLOv8 (TEYOLOv8) network to improve detection and classification of seven dental classes. The model enhances localization and segmentation accuracy by embedding an attentive transformer mechanism into the YOLOv8 framework. The TEYOLOv8 architecture integrates data augmentation, feature localization, segmentation, and classification. It employs C2f (Cross-Stage Partial Focus) convolutional blocks to preserve partial segmentation features and introduces a C2fAttn (Cross-Stage Feature Attention) module to capture fine-grained spatial details such as s... More >