# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
## 3 Automatic Detection Method Development
### The Backbone Network Is Modified with EfficientNetV2  
With the advancements in research on CNNs, the evaluation criteria for a good model have shifted beyond just stacking network layers to improve the accuracy. The ability to achieve satisfactory results with fewer parameters and faster training speeds has become equally important. EfficientNet is a lightweight network structure that addresses these concerns [24]. EfficientNetV2 is an enhanced network built upon the fundamental concept of EfficientNet. [PERSON] et al. modified the depth, width, and input image resolution parameters, and the network achieved improved performance [25]. This classification recognition network utilizes a neural architecture search (NAS) and a composite model expansion method to select the optimal composite coefficients that proportionally scale the network's depth, width, and input image resolution. These adjustments are made to obtain optimal parameters that maximize the recognition feature accuracy. EfficientNetV2 effectively reduces the number of parameters and the complexity of model training by dynamically balancing these three dimensions and delivers a noticeable improvement in the inference speed. The EffcientNetV2 network primarily consists of Mobile Inverted Bottleneck Convolution (MBConv) and Fused-MBConv modules (see Figure 5) stacked together, and its specific structure is detailed in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline
**Stage** & **Operator** & **Stride** & **\#Channels** & **\#Layers** \\ \hline
0 & Conv3\(\times\)3 & 2 & 48 & 1 \\
1 & Fused-MBConv1, k3\(\times\)3 & 1 & 32 & 2 \\
2 & Fused-MBConv4, k3\(\times\)3 & 2 & 64 & 4 \\
3 & Fused-MBConv4, k3\(\times\)3 & 2 & 128 & 4 \\
4 & MBConv4, k3\(\times\)3 & 2 & 256 & 6 \\
5 & MBConv6, k3\(\times\)3 & 1 & 512 & 9 \\
6 & MBConv6, k3\(\times\)3 & 2 & 1024 & 15 \\
7 & Conv1\(\times\)1 and Pooling and FC & - & 2048 & 1 \\ \hline \end{tabular}
\end{table} 
Table 1: EfficientNetV2 architecture—MBConv and Fused-MBConv blocks described in Figure 5.  
To improve the computational speed of the network, the following module configurations are implemented for the processing of the data. The input images have a size of 1295 \(\times\) 620 pixels with three channels, which undergo several transformations throughout the network. Stages 1, 2, and 3 are carried out by the Fused-MBConv modules, as listed below. Fused-MBConv Modules: These modules are used for faster computation. The Fused-MBConv modules replace the 1 \(\times\) 1 expansion convolution structure in the MBConv modules with a standard 3 \(\times\) 3 convolution structure. Additionally, they replace both the 1 \(\times\) 1 expansion and 3 \(\times\) 3 depthwise convolution with a regular 3 \(\times\) 3 convolution structure. This configuration avoids the slowdown caused by depthwise convolution in shallow layers [25]. Specifically, the input image size and channel numbers change as follows. Stage 1: Input size \(\sim\)323 \(\times\) 155 pixels, 3 channels \(\sim\) 32 channels. Stage 2: Input size \(\sim\)161 \(\times\) 78 pixels, 32 channels \(\sim\) 64 channels. Stage 3: Input size \(\sim\)80 \(\times\) 39 pixels, 64 channels \(\sim\) 128 channels. Stages 4, 5, and 6 are carried out by deep convolution, as listed below. MBConv Modules: These modules consist of two 1 \(\times\) 1 convolutions (input and output), a 3 \(\times\) 3 depth-separable convolution, and a Squeeze-and-Excitation (SE) attention module [26]. The SE module enhances the feature extraction capability by modifying the weights of each channel, thereby increasing the attention towards the most important channels. The transformations in the deep layers are as follows. Stage 4: Input size \(\sim\)40 \(\times\) 20 pixels, 128 channels \(\sim\) 256 channels. Stage 5: Input size \(\sim\)20 \(\times\) 10 pixels, 256 channels \(\sim\) 512 channels. Stage 6: Input size \(\sim\)10 \(\times\) 5 pixels, 512 channels \(\sim\) 1024 channels.