# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 4 Experiment
### Experiments and Results Analysis  
The experiments were started according to the above configuration, and all networks in the experimental process were subjected to transfer learning in combination with pre-trained weight models according to the method introduced in Section 3.4. After testing, this method can improve the training effect without increasing the training cost in the task of this paper. In the experiments, we tested the performance metrics under each SNR according to the above dataset configuration and created a line graph with SNR as the horizontal coordinate and specific performance metrics as the vertical coordinate for analysis. Figure 10 shows the output of different models for the same graph during the experiment, and it can be easily seen that the improved model avoids some of the initial false detection cases. More specifically, as indicated by the thick red rectangular box in the figure, the locations where the test results change at different stages are shown here. In the detection result of the Baseline model, there are three target boxes in the thick red box of the first image, and one of the target boxes clearly overlaps the other target boxes. We can easily determine that the extra overlapping target box is caused by wrong detection. The improved detection result leaves only two correct target boxes, which proves that our improvement is effective. In addition, we also calculated the average value of each performance index between SNR 0 and 16 dB, as shown in Table 2.  
The average performance of the four methods shows that the performance of the proposed method in this paper is improved in all aspects, which proves the effectiveness of the method. Figure 11 shows the comparison of the detection probabilities obtained for the models at different stages of this paper with different signal-to-noise ratios of the test set. As shown in Figure 11, we compare the performance of the four neural network models under different SNR conditions. Since the model with the best results in previous studies is the YOLOv3 + CIOU model proposed in the article [10], and through our test, YOLOv5 performs better than YOLOv3 in this task. Therefore, we use the YOLOv5 + CIOU model as our Baseline. Overall, the detection probability increases with the increase in signal-to-noise ratio (SNR). The experimental results demonstrate that the improved model with the added BiFPN and EIOU module can effectively improve the performance of target detection, and the detection probability increases by about 3-4% in the range of SNR of 0-16 dB. The model improved by the EIOU loss function is also able to improve the target detection performance, with a detection probability improvement of about 2-3% in the range of 0-16 dB. Obviously, BiFPN is more efficient in terms of improvement of detection probability compared to EIOU. Finally, by comparing the final model proposed in this paper with the model improved by the Baseline + BiFPN module, we find that in the range of SNR of 4-16 dB, there is no marked difference in the performance between the two models in terms of detection probability, and the effects are considered to be same. However, in the range of SNR of 0-4 dB, the final model proposed in this paper has significantly higher \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Average SNR Performance between 0 and 16 dB**} \\ \cline{2-4}  & \(\mathbf{P_{d}}\) & \(\mathbf{P_{f}}\) & \(\mathbf{E_{avg}}\) \\ \hline Baseline & 90.25\% & 9.87\% & 2.06\% \\ Baseline + BiFPN & 93.63\% & 11.31\% & 1.43\% \\ Baseline + EIOU & 93.21\% & 9.94\% & 1.42\% \\
**Baseline + BiFPN + EIOU** & **94.09\%** & **9.79\%** & **1.38\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the average parameter indicators of the four methods. Figure 10: Comparison of the detection of different models. performance with an increase in detection probability of about 1-3%.