## 4 Testing Experiment
### Evaluation Framework  
To comprehensively evaluate the performance of the YOLOv11-ECA model in object detection and pose classification tasks, this study adopts a multi-dimensional evaluation framework encompassing detection accuracy, computational complexity, and multi-object tracking stability. In terms of detection performance, key evaluation metrics include the precision-recall (PR) curve, F1 curve, and [EMAIL_ADDRESS]. The PR curve illustrates the trade-off between detection precision and recall at varying confidence thresholds. For each class, the  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Value** & **Clarification** \\ \hline Batch & 4 & Number of samples per training \\ Imgsz & 640 & Size of the training image \\ Epochs & 1000 & Total training rounds \\ Lr0 & 0.01 & Learning rate \\ Optimizer & AdamW & Optimiser selection \\ Weight\_decay & 0.0005 & Preventing overfitting \\ Momentum & 0.937 & Momentum factor \\ Amp & True & Enable automatic mixing accuracy \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters for YOLOv11 training.  
model outputs predicted bounding boxes and associated confidence scores. By adjusting the confidence threshold, different pairs of precision and recall can be computed to generate the PR curve. The area under this curve represents the Average Precision (AP) for the corresponding class, which quantitatively reflects the overall detection performance of that class. The F1 curve captures the trend of F1 scores across different thresholds, indicating the model's balanced performance between precision and recall. By averaging the AP values across all classes, the mean Average Precision at an Intersection over Union (IoU) threshold of 0.5 ([EMAIL_ADDRESS]) is computed and used as the principal metric to assess overall detection accuracy.  
For evaluating the model's computational complexity and multi-object tracking performance, this study employs metrics such as FLOPs (Floating Point Operations), MOTA (Multiple Object Tracking Accuracy), and IDF1 (ID F1 Score). FLOPs quantify the number of floating-point operations required during a single forward inference pass. A lower FLOPs value indicates a more lightweight model, making it more suitable for real-time inference scenarios. MOTA provides an overall measure of tracking accuracy by accounting for missed detections, false positives, and identity switches. IDF1 evaluates the consistency of identity preservation by computing the F1 score based on correct target ID matches, thereby reflecting the quality and robustness of the tracking process.  
\[\text{MOTA}=1-\frac{\sum_{t}(\text{FN}_{t}+\text{FP}_{t}+\text{IDSW}_{t})}{ \sum_{t}\text{GT}_{t}} \tag{8}\]  
where FN\({}_{t}\) is the number of false negatives at frame \(t\), FP\({}_{t}\) is the number of false positives, IDSW\({}_{t}\) denotes ID switches, and GT\({}_{t}\) is the number of ground truth objects.  
\[\text{IDF1}=\frac{2\cdot\text{IDTP}}{2\cdot\text{IDTP}+\text{IDFP}+\text{ IDFN}} \tag{9}\]  
where IDTP is the number of correctly identified detections, IDFP is the number of false positive identities, and IDFN is the number of missed ground-truth identities.  
Based on the aforementioned evaluation metrics, this study conducts a comprehensive assessment of the proposed model from multiple perspectives, including detection accuracy, recall capability, inference efficiency, and multi-object tracking stability. This systematic evaluation ensures the completeness and reliability of the experimental results.