## 4 Discussion  
The aim of this paper is to investigate the use of computer vision-related techniques for the detection and classification of impacted mandibular third molar and to improve the accuracy of the model while ensuring that the model is lightweight through the technique of knowledge distillation. In our study, we used YOLOv5s as the teacher model and YOLOv5s as the student model, resulting in a 2.9% improvement in the accuracy of the final model and some reduction in the number of model parameters and computational effort. This paper also compares our model with mainstream target detection networks, and the experimental results show that the proposed method performs best. Regarding the feasibility of the proposed method, this paper discusses the following. Firstly, panoramic radiography is more suitable for the application of grass-roots hospitals because of its low-dose radiation and low price, so panoramic radiographs are chosen as the dataset of this study. Compared with CBCT, panoramic radiographs can only reflect the two-dimensional anatomical relationship between the root and the inferior alveolar canal. CBCT can finely display the relationship between the mandibular impacted third molar and the inferior alveolar canal, including buccal and lingual position and the presence of bone septum, so as to predict the possibility of injury to the inferior alveolar nerve during the operation. However, the price of CBCT is high, and most impaction cases can be correctly judged by panoramic radiographs. Therefore, this study chooses panoramic radiographs as the dataset, which has better universality. To sum up, the auxiliary diagnosis method should be selected according to the specific situation in the clinic. In the future, we will do further research on the application of deep learning in CBCT. Secondly, our model is based on YOLOv5s, which has the advantage of being lightweight with a smaller model size and shorter training time, allowing it to perform excellent detection and classification tasks with limited resources. YOLOv5s is less demanding in terms of hardware requirements, which makes it more convenient for oral health professionals to deploy and apply, and has better generalizability. Thirdly, this paper used knowledge distillation techniques on top of YOLOv5s to transfer the knowledge from YOLOv5s to the YOLOv5s model, which improves the mAP of YOLOv5s by 2.9% while ensuring the model is lightweight. Our experimental results show that the YOLOv5s-x model outperforms mainstream target monitoring networks such as DETR, YOLOv7, and YOLOv8. Although DETR, YOLOv7, and YOLOv8 are all newer detection models than YOLOv5, they also have some problems. DETR has the largest and largest model size and the lowest accuracy rate for detection classification. YOLOv7 has the smallest accuracy difference from YOLOv5 but is still inferior to our model in terms of a number of parameters and computational effort. This study also found that YOLOv8n had the smallest number of parameters and computational effort, but the mAP was 1.2% lower than YOLOv5s-x. YOLOv8s was slightly worse than YOLOv5s-x in all metrics. Insummary, the YOLOv5s-x model is more suitable for the detection and classification of impacted mandibular third molars. Finally, although our model has improved in performance, there are still some issues that need further improvement. Firstly, the mAP of our model is 0.925, which still has more room for improvement. Secondly, the performance of our model varies in different environments, and therefore further optimization is needed. Finally, there is still room for our model to further reduce the number of model parameters and computational effort, making the benefits of making a model lightweight more prominent. In summary, our study demonstrates the feasibility of utilizing YOLOv5 for the detection and classification of impacted mandibular third molars, with the potential to enhance model performance through the application of knowledge distillation techniques.