# A Dynamic Measurement System Based on Adaptive Clustering and Multi-Classifier
## 4 Results
### Visual Simulation Platform Experiment  
The commonly used metrics for evaluating target detection methods are accuracy and success rate. Accuracy is measured by initializing the first frame of the dataset with the annotated true position and then calculating the average overlap rate between each method's detection box and the annotation box across all frames. Success rate, on the other hand, is assessed by calculating the proportion of frames successfully detected by each method in relation to the total number of frames in the dataset. These metrics serve as the foundation for data evaluation, verification, and analysis in comparative experiments involving target tracking and positioning algorithms.  
The algorithm in this paper is an improvement on the YOLOv5 + DeepSort framework [8]; hence, it is necessary to compare it with YOLOv5 in target detection experiments. Additionally, the selection of comparison algorithms in this paper is based on their real-time performance and portability. Algorithms that meet the criteria of processing speeds over 30 frames per second and that are feasible to replicate on hardware can be considered potential comparison algorithms. The following eight algorithms, which show the best results in target detection experiments, were selected for comparison.  
Figure 5: Measurement system overview schematic.  
My method is compared with several widely utilized approaches, including SSD (Single Shot MultiBox Detector) [9], MDNet (Learning MultiDomain Convolutional Neural Networks for Visual Tracking) [10], TCNN (Tube Convolutional Neural Network for Action Detection in Videos) [11], YOLOV5 (Real-Time Vehicle Detection from UAV Aerial Images Based on Improved YOLOv5) [12], YOLOv3 (A rapid field crop data collection method for complex cropping patterns using UAV and YOLOv3) [13], C-COT (Learning Continuous Convolution Operators for Visual Tracking) [14], FSAF (Feature Selective AnchorFree Module for SingleShot Object Detection) [15], RetinaNet (Multiscale Maize Tassel Identification Based on Improved RetinaNet Model and UAV Images) [16], and CornerNet (CornerNet: Detecting Objects as Paired Keypoints) [17]. These comparisons provide a deeper insight into the strengths and weaknesses of each method within the target detection landscape.  
Figure 6 depicts the accuracy and success rate of various methods. Within the dataset, threshold conditions are defined as follows: the accuracy threshold is set at 20 pixels, and the success threshold is determined by an overlap rate of 0.5 between the detected and the actual values. Notably, the algorithm discussed in this paper achieved the highest success rate of 95.2% and an accuracy of 85.1%, substantially surpassing other conventional algorithms. Based on these findings, it can be concluded that the target detection method proposed in this study not only maintains high success rates but also ensures robust accuracy.  
Figure 7 displays the outcomes of target detection and tracking performed using the algorithm proposed in this paper on the simulation dataset. The simulation dataset features a rich and diverse background, and the refueling drogue is characterized by significant features and an appropriate size within the image. These factors have allowed various deep learning algorithms to demonstrate effective results on this dataset. Consequently, the distinctive advantages of the algorithm described in this article are subtle and primarily manifest in the demonstrated detection and tracking performance.  
Figure 6: Accuracy vs. success curve.  
Figure 7: _Cont_.