# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
## 4 Results
### Evaluation Indicators  
In this study, the performance of the model is evaluated based on both its detection accuracy and detection speed. The number of frames detected per second (FPS) is employed as a measure of the detection speed, while the mean average precision (mAP) is computed to assess the accuracy of the model.  
The dataset comprises both negative class samples (N) and positive class samples (P). The combinations of N and P, along with their corresponding meanings, are outlined in the following explanation. When the threshold exceeds the Intersection over Union (IoU), it indicates correct predictions by the model. Conversely, when the threshold falls below the IoU, it signifies incorrect predictions. FN, FP, and TN are the false negatives, false positives, and true negatives, respectively.  
Precision is a metric that measures the accuracy of a model. It represents the proportion of true positive cases among the samples predicted as positive, as shown in Equation (6). A higher precision value signifies the greater accuracy of the model, indicating that a larger proportion of the predicted positive cases are indeed true positives.  
\[pre\text{cision}=\frac{TP}{FP+TP} \tag{6}\]  
mAP is a widely used evaluation metric in the field of object detection. It quantifies the average detection accuracy of a model across multiple classes or categories. A higher mAP indicates higher average accuracy in detecting objects, demonstrating the better performance of the model in object detection tasks. Specifically, mAP\({}_{0.50:0.95}\) refers to the mean average precision calculated at IoU thresholds ranging from 0.5 to 0.95, while mAP\({}_{0.5}\) refers to the mean average precision at an IoU threshold of 0.5.  
\[mAP=\frac{1}{m}\sum_{i=1}^{N}P(k)\Delta R(k) \tag{7}\]  
In Equation (7), N is the total number of samples in the test set, P is the precision, k is the kth sample, and R is the amount of change in the recall rate from the k-1 st sample to the kth sample. The recall rate, also known simply as recall, is a metric that measures the ability of the model to identify all relevant instances in the dataset. It is defined as the proportion of true positive cases among the total actual positive cases. Higher recall indicates that the model can detect a larger portion of the true positive cases.  
\"Params/M\" denotes the number of parameters in millions. This metric is used to compare the model's complexity and efficiency. Lower Params/M values indicate more lightweight models, which are often preferable for applications requiring limited computational resources or faster inference times.  
FPS is a valuable metric for the evaluation of the inference speeds of object detection algorithms. It provides a straightforward measure of how many frames can be processed per second by the detection algorithm. In the context of object detection, the objective is to accurately identify and locate objects within a video or image sequence.  
By measuring the FPS, we can assess the efficiency and real-time capability of different object detection algorithms. A higher FPS indicates the faster processing of frames, which is crucial for applications requiring real-time detection. Evaluating the FPS allows for direct comparisons between algorithms, helping to determine their efficiency and suitability for real-time applications. For CSES data, while real-time processing might not be immediately necessary, the efficient processing of large datasets is vital. A higher FPS means that our algorithm can handle extensive datasets swiftly, which is beneficial for timely analysis. Additionally, future CSES missions may require real-time or near-real-time processing, making the FPS an important metric in assessing the computational efficiency and scalability, ensuring preparedness for future scenarios where rapid data processing is critical.