## 1 Introduction
[16] conducted a study to evaluate the effectiveness of simulation training on medical students' intravenous (IV) intubation skills. Studies have shown that novices' injection skills significantly improved after simulator training, and these findings have been replicated in other medical student populations as well [4; 19]. Furthermore, ongoing education could also help to build confidence and proficiency in novice healthcare professionals [20]. However, these studies did not demonstrate a significant difference in the accuracy of intravenous injections. YOLOv7 [21] is an advanced object detection model that can be trained on various types of images. With its fast and accurate object detection capabilities, YOLOv7 can be helpful in many medical applications. For example, [PERSON]. [PERSON] et al. [22] devised a deep learning mode requiring the YOLOv7 framework to detect fetal hearts (measuring approximately 4-8 cm) in ultrasound videos in real-time. The effectiveness of YOLOv7 in detecting small objects was confirmed through validation studies [23; 24] and has the potential to aid medical professionals in accurate medical diagnoses. [PERSON] et al. [25] evaluated the performance of the YOLOv5 and YOLOv7 models, along with DeepSORT, for droplet identification and tracking in microfluidics. Their results showed that YOLOv7 is faster, but lighter YOLO models can achieve real-time tracking. [PERSON] et al. [26] developed an image recognition system for detecting dental instruments during treatment to prevent injuries and leftover instruments. YOLOv4 and YOLOv7 were used, with mean detection accuracy ranging from 85.3% to 89.7%. These studies not only demonstrate the faster detection speed and detection accuracy of YOLOv7, but also highlight its efficacy as a detection method for identifying instrument components. This study aims to address the issues highlighted in the aforementioned research by proposing an automatic puncture time detection method that utilizes the YOLOv7 algorithm. The aim of the proposed methodology is to detect time-related parameters in simulated injection training and obtain characteristic parameters such as puncture depth through post-processing. The major contributions of this study can be outlined as follows:  
1. Develop a novel multi-camera data acquisition system capable of capturing video data directly from medical injection education alongside relevant parameters, including operation time and reverse blood detection. 2. The area of significance for the injection operation is manually demarcated, followed by the application of image processing techniques such as image rotation and segmentation to train an automated needle detection model based on YOLOv7. 3. Utilize the trained model to calculate the original needle length and puncture timing of injection, thereby providing a novel evaluation metric for medical injection training feedback and a crucial technique for establishing a subsequent evaluation system. Section 2 provides an overview of the materials and data collection, including the design and implementation of the multi-camera data acquisition system. Section 3 details the YOLOv7 algorithm for clinical injection needle detection and the image processing techniques employed. Section 4 presents the experimental results, analyzes the errors, and verifies the robustness of the study. Section 5 provides a comprehensive discussion of the conclusions and implications of the study, including potential future research directions.