# A Dynamic Measurement System Based on Adaptive Clustering and Multi-Classifier
## 4 Results
### Physical Flight Experiment of UAVs  
This subsection of the experiment primarily focuses on comparing the target tracking capabilities of various algorithms. The algorithm discussed in this paper represents an improvement on the YOLOv5 + DeepSort framework; thus, it is necessary to compare it with DeepSort in the target tracking experiments. Additionally, the selection of comparison algorithms is based on their real-time performance and portability. Algorithms that can process over 30 frames per second and whose complexity is suitable for replication on hardware can be considered candidates for comparison. The following eight algorithms, which have demonstrated the best target tracking results, were selected for comparison. To assess the accuracy of the algorithm proposed in this paper, we conducted comparisons with widely utilized methods on the drone dataset. These methods include (Development of a YOLO-KCF Coupling Algorithm for Miniature Fixed-Wing UAVs in Target Detection and Tracking) [18], MDNet (Learning MultiDomain Convolutional Neural Networks for Visual Tracking), TCNN (Tube Convolutional Neural Network for Action Detection in Videos), GOTURN (Target Adaptive Tracking Based on GOTURN), SiamPC (SiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks) [19], C-COT (Learning Continuous Convolution Operators for Visual Tracking), ECO (Efficient Convolution Operators for Tracking) [20], DeepSort (Simple Online and Realtime Tracking with a Deep Association Metric) [21], and UPDT (Unveiling the Power of Deep Tracking) [22]. Figure 8 illustrates the comparison results across multiple datasets, where each row signifies a certain type of dataset categorized by working distance: near, medium, and far. From the bounding boxes displayed in the visualizations, it is deduced that the refueling drogue target is small, moves rapidly, and is significantly affected by complex backgrounds. In most cases, tracking algorithms tend to lose the target near the initial region and continue to track and incorrectly locate targets without the capability of correction; this observation holds true across other datasets. However, algorithms like DeepSort demonstrate effective target detection and tracking in some test images. In a study involving a physical dataset with six attributes, a pairwise combination generated nine sets of datasets from left and right perspectives. Detection and tracking performance were analyzed for each dataset at a rate of 30 frames per second within the video stream. The proportion of frames that failed to locate the refueling drogue using both our method and the comparative methods served as the criterion for assessing the missed detection rate. The evaluation outcomes for the four groups are detailed in Tables 1 and 2. According to the data presented in the table, the missed detection rate of our algorithm across each dataset does not exceed 4%, thereby ensuring long-term effective detection and tracking. The average missed detection rate of our algorithm, at 2%, is significantly lower than the original algorithm's 11%. Furthermore, our algorithm demonstrates enhanced robustness and stability and notably improves the detection and tracking of fuzzy, small targets. Figure 7: Algorithm effect display diagram. This paper conducts a comprehensive feature response analysis on the mentioned dataset and statistically analyzes the target feature response values for each frame across the nine datasets. The Fourier transform peak of the refueling drogue in each image frame was utilized as the criterion for evaluating feature response value data. Figure 9 clearly illustrates the effectiveness of our algorithm in various application settings, from both left and right perspectives, respectively. \begin{table}
\begin{tabular}{c c c} \hline
**Dataset Number** & **My Method (\%)** & **YOLO + DeepSort (\%)** \\ \hline dataset 4 & 2.77 & 18.13 \\ \hline dataset 5 & 0.96 & 6.73 \\ \hline dataset 6 & 0.41 & 18.5 \\ \hline dataset 7 & 2.38 & 20.63 \\ \hline \end{tabular}
\end{table}