# A Dynamic Measurement System Based on Adaptive Clustering and Multi-Classifier
## 4 Results
### Method Performance Analysis Experiment
#### 4.4.2 Target Localization Timeliness Experiment  
The purpose of the experiment in this section is to compare the differences in timeliness between the algorithm proposed in this paper and a comparative algorithm. Since this paper adds several modules to the original YOLOv5 algorithm, such as the feature responder, decision function, and state judgment module, these additions theoretically affect the processing speed of target detection and tracking. Therefore, by analyzing the response time, we aim to better evaluate the computational processing capability of the method proposed in this paper. The comparative algorithm chosen for this section is the YOLOv5 algorithm, which has demonstrated excellent performance in response time. Additionally, we have incorporated ablation algorithms to conduct timeliness experiments. By comparing the timeliness of My Method A/B/C, we can obtain a relatively clear analysis of the computing resources required by each module. This analysis plays a crucial role in elucidating the practical feasibility and scalability of the methods presented in this paper. For this experiment, we selected a test set with significant changes in target shape, the longest flight range, and instances of trailing during the refueling drogue movement.  
From Figure 11, it can be observed that when processing images with a resolution of 1920 \(\times\) 1080 in an NVIDIA RTX 3080 environment, the response time of the YOLO algorithm averages about 0.03 s, fluctuating within a range of \(\pm\)0.003 s, indicating general stability. The response time of the algorithm discussed in this paper averages about 0.032 s, fluctuating within a range of \(\pm\)0.002 s, with three instances where the response time significantly increased. These increases primarily occurred due to the adaptive selection of the optimal classifier. Overall, in terms of response time, the proposed algorithm is slightly inferior to the YOLO algorithm, and its stability is also less consistent compared to YOLO. However, the timeliness still meets the processing requirements of 30 frames per second for the aerial refueling project. Future considerations include the use of parallel processing and fitting filters to enhance the timeliness of the adaptive classifier selection.  
Figure 10: Comparison results of positioning deviation.  
In addition, it can be observed that the ablation algorithm of My Method A, which includes only the addition of a feature responder, functions effectively only when the confidence in some detection and tracking results is low, exhibiting a jump in response time with an average jump duration of about 0.045 s. Method B, which only added a decision function, has an average response time that is 0.0011 s higher than the YOLO algorithm, while Method C, which only added a state judgment module, has an average response time that is 0.0012 s higher than the YOLO algorithm. It is evident that the computational processing of the proposed algorithm mainly concentrates on the adaptive switch of classifiers at the feature responder, where the complexity of the feature responder exceeds that of the decision function and state judgment module. Additionally, in the proposed algorithm, it is not necessary to invoke these three modules for processing each frame image, especially the feature responder, which is generally only involved in the processing of a few frames. The decision function and state judgment module generally participate in the computation only when the target detection and tracking results are too low. Averaged across each frame, the combined response time of the three modules accounts for only 6.25% of the total response time. To clearly demonstrate the performance balance of the proposed method in terms of timeliness, we have created a bar graph to represent this balance, as shown in Figure 12.  
Figure 11: Response time for my method vs. YOLO.  
Figure 12: Balanced view.