Joint Detection System for Natural Teeth and Dental Implants Based on Attention Mechanism YOLOv12

This article introduces an innovative computer vision project that combines the YOLOv12 object detection algorithm with an attention mechanism to achieve simultaneous and accurate recognition of natural teeth and dental implants in panoramic X-ray images. The project aims to solve the problems of time-consuming and labor-intensive traditional manual film reading, as well as missed diagnoses and misdiagnoses due to differences in experience. It has important clinical value for preoperative evaluation, postoperative review, and long-term maintenance in stomatology.

In the field of stomatology, panoramic X-ray images are important tools for dentists to diagnose and formulate treatment plans. The traditional manual film reading method is time-consuming and labor-intensive, and it is prone to missed diagnoses or misdiagnoses due to differences in doctors' experience. With the popularization of dental implants, accurately distinguishing between natural teeth and artificial dental implants has important clinical value for preoperative evaluation, postoperative review, and long-term maintenance. Dental implants appear as high-density metal/ceramic materials on X-ray images and lack pulp cavity structures. Accurately identifying these features can help doctors quickly assess oral conditions and formulate personalized treatment plans.

This project is based on the latest YOLOv12 object detection architecture, which has significant advantages in end-to-end detection and real-time performance. The core innovation lies in the introduction of the attention mechanism, which originated from natural language processing and is now widely used in computer vision. It can dynamically focus on key areas of the image (anatomical structures of natural teeth or high-density features of dental implants). The attention module is embedded into the YOLOv12 network structure, generating a weight matrix by calculating the correlation of feature maps to enhance the response of important features and suppress background noise. The model architecture is divided into three parts:

1. Backbone Network: Adopts an improved CSPDarknet structure. Cross-stage local connections and residual connections enhance feature extraction capabilities, and attention modules are embedded in multiple layers;
2. Feature Pyramid Network (FPN): Fuses multi-scale features, supporting the detection of targets of different sizes through upsampling and lateral connections;
3. Detection Head: Outputs bounding box coordinates, target confidence, and category probabilities. The attention mechanism assists in the accurate positioning of dense tooth areas.

The project uses a panoramic X-ray dataset covering different ages, genders, and oral conditions (healthy teeth, lesions, missing teeth, etc.) for training and validation. Data preprocessing includes standardization (unifying grayscale distribution) and data augmentation (random rotation, scaling, brightness adjustment, etc.) to expand samples and improve generalization ability. Training needs to learn the features of natural teeth and dental implants simultaneously. A multi-task learning framework is adopted, which combines classification loss, localization loss, and confidence loss in a weighted manner to achieve end-to-end joint optimization.

The evaluation metrics for the test set include mAP, precision, recall, and FPS. The results show that the YOLOv12 model with the attention mechanism has excellent performance, and its mAP is significantly higher than that of traditional two-stage detectors and early YOLO versions. The clinical application prospects are broad:

• As an auxiliary diagnostic tool for dentists, it automatically marks teeth and dental implants to improve film reading efficiency;
• Integrated into the image management system of stomatological hospitals to realize batch processing and intelligent archiving;
• The principle can be extended to other oral image analysis tasks such as caries detection and periapical lesion recognition.

Practical applications face challenges: The quality of panoramic X-ray images is affected by shooting technology and patient cooperation, and low-quality images may lead to detection failure; the image performance of dental implants from different brands varies, so the model needs continuous learning to adapt to new types. Future research directions:

• Introduce semi-supervised learning to reduce dependence on labeled data;
• Explore 3D CT image detection methods to obtain spatial information;
• Develop lightweight models to adapt to mobile devices and edge computing;
• Establish multi-center verification to evaluate the generalization performance of the model.

The joint detection project of natural teeth and dental implants based on attention mechanism YOLOv12 demonstrates the great potential of deep learning in stomatological medical image analysis. By combining advanced object detection algorithms with medical professional knowledge, it provides a valuable reference for the development of intelligent oral diagnosis systems. With the progress of AI technology, we look forward to more innovative applications to make medical services more accurate and efficient.