# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
## 4 Results
### Algorithm Performance Assessment  
To comprehensively evaluate the performance of the improved models, comparative experiments are conducted on multiple models using the same randomly partitioned dataset. The experimental results of each model are recorded after 300 epochs, utilizing the mean average precision (mAP) and detection speed (FPS) as the evaluation metrics. By using the same dataset and experimental setup for all models, a fair and direct comparison can be performed. The mAP metric provides insight into the accuracy and precision of object detection, while the FPS metric allows for the assessment of the speed at which the models can process frames. By considering both the mAP and FPS as evaluation metrics, the experiments provide a balanced assessment of the improved models, considering their accuracy as well as their computational efficiency in terms of speed.  
Overall, this experimental setup ensures a comprehensive evaluation of the models, encompassing their performance in terms of accuracy and speed.  
As shown in Table 2, our proposed QP-DETR model reduces the parameter count by 17%, resulting in a compact size of 34.27 million. It surpasses other algorithms in detection speed, matches YOLOv3 in performance, and enhances the accuracy by 1% compared to DETR-R50. Collectively, these findings affirm QP-DETR's outstanding capabilities in detecting QP events. The QP-DETR model amalgamates superior accuracy, reduced parameters, and swift detection speeds, rendering it a potent and efficient solution for QP event detection tasks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Backbone** & **Params/M** & **FPS** & **mAP\({}_{0.50.95}\)** & **mAP\({}_{0.5}\)** \\ \hline RetinaNet & ResNet50 & 36.47 & 35.6 & 0.417 & 0.835 \\ YOLOv3 & Darknet-53 & 61.52 & **43.5** & 0.468 & 0.879 \\ Faster RCNN & ResNet50 & 41.44 & 21.2 & 0.403 & 0.763 \\ CenterNet & Hourglass-52 & 104.8 & 16.8 & 0.436 & 0.829 \\ DETR-R50 & ResNet50 & 41.28 & 28.4 & 0.458 & 0.914 \\ DETR-R101 & ResNet101 & 60.22 & 17.9 & 0.475 & 0.920 \\ QP-DETR & EfficientNetV2 & **34.27** & 39.3 & **0.480** & **0.923** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of algorithms and backbone models. Yellow highlighting is the optimal value.  
As shown in Table 3, after replacing the backbone with EfficientNetV2, we find a significant reduction in the number of parameters, from 41.28 M to 34.27 M parameters, and a 0.2% increase in the mAP. This is because EfficientNetV2 has stronger feature extraction capabilities and a lighter design compared to ResNet50. Subsequently, when incorporating ECA channel attention into the model, the mAP\({}_{0.5}\) showed a further improvement of 0.4%. This addition enhanced the model's ability to refine feature extraction, resulting in increased detection accuracy. Following this, the inclusion of DefAttention led to an additional increase in the mAP\({}_{0.5}\) of 0.3%. DefAttention incorporated a variable attention mechanism, enabling the model to extract more precise features, thus contributing to improved detection performance.  
Although there were no significant changes in the mAP\({}_{0.5}\) observed after applying multi-level fusion with DefAttention, there was an improvement of 0.4% in the mAP\({}_{0.50.95}\) metric. This indicates that the model achieved better precision in localizing and identifying objects with high confidence. Overall, these improvements in the mAP\({}_{0.5}\) and mAP\({}_{0.50.95}\), achieved through various modifications such as backbone replacement, channel attention, and DefAttention, demonstrate the effectiveness of the proposed enhancements in accurately detecting and localizing objects in the study.