###### Abstract  
The extraction of impacted third molars is one of the most common dental operations. When the impacted third molar is extracted, the operation plan is generally different because of the different impacted positions of the tooth. Therefore, judging the impacted type of the third molar is the basis of the third molar extraction operation. At present, oral health professionals usually analyze panoramic radiographs to determine the types of impacted third molars, but the diagnosis is easily affected by oral health professionals' subjective consciousnesses. Computer vision technology can help doctors analyze medical images faster and more accurately, so it is very desirable to use computer vision to detect and classify the impacted third molars. Based on the panoramic radiographs of the School of Stomatology, Lanzhou University, this paper establishes an object detection dataset containing six types of impacted third molars. On the basis of this dataset, the lightweight third molar impacted detection and classification model is studied in this paper. This study introduces the method of knowledge distillation on the basis of YOLOv5s and uses YOLOv5x as the teacher's model to guide YOLOv5s, which not only ensures the light weight of the model but also improves the accuracy of the model. Finally, the YOLOv5s-x model is obtained. The experimental results show that the introduction of knowledge distillation effectively improves the accuracy of the model while ensuring its light weight, the mAP of YOLOv5s-x is increased by 2.9% compared with the original model, and the amount of parameters and calculations is also reduced to a certain extent. Compared with mainstream object detection networks, including YOLOv8, YOLOv5s-x also has certain advantages, which can provide oral health professionals with better impacted third molar detection and classification services. disease, tumors, or root resorption [4; 5; 6; 7]. Consequently, the extraction of third molars has become one of the most frequently performed surgical procedures by oral and maxillofacial surgeons [8]. The position of the impacted mandibular third molar presents unique challenges. It is situated in close proximity to important anatomical structures and is closely associated with adjacent teeth, making extraction surgery often difficult [9]. Additionally, surgical procedures can easily lead to complications such as reactive pain and swelling after tooth extraction [10]. Therefore, it is necessary to develop an appropriate and comprehensive surgical plan prior to the procedure. The impacted mandibular third molar can be categorized into different positions, including vertical, mesoangular, and horizontal positions, among others. Each position presents varying difficulties for tooth extraction. Consequently, preoperative evaluation of the position of the third molar plays an important role in determining the specific surgical plan [11]. Usually, oral health professionals can determine the position of the impacted mandibular third molar by analyzing panoramic radiographs. However, the traditional way for oral health professionals to analyze panoramic radiographs increases their daily workload and reduces their diagnostic and treatment efficiency. What is more, the determination of the position of mandibular impacted third molars is also susceptible to subjective factors such as experience level and fatigue level, resulting in many uncertainties. Therefore, an automated, low-cost, accurate, and rapid model for the detection and classification of impacted third molars is urgently needed. Artificial intelligence (AI) was founded by [PERSON] et al. in the 1950s [12], referring to a branch of computer science in which predictions are made using machine methods to mimic what humans may do in the same situation. Deep learning is a special form of machine learning based on artificial neural networks (ANNs) inspired by the human nervous system [13].