# Dark-Center Based Insulator Detection Method in Foggy Environment
## 4 Experimental Results and Analysis
### Target Detection Experiments
#### 4.2.4 Experimental Results  
To verify the superiority of the method in this paper, five representative and well-performing feature pyramid methods were selected for comparison in the experiments, including SPP ([PERSON], 2015), ASPP ([PERSON], 2017), PPM ([PERSON], 2017), RFB and RFB-s ([PERSON], 2018), and Strip Pooling ([PERSON], 2020). The number of input and output channels was controlled to be 2048 in the experiments, and both input channels were first transformed to 128 via a1 convolution to ensure that the number of parameters of each model was kept to (33, 35). The experimental results are shown in Table 3.  
As seen in Table 3, by comparing the existing SPP-like models, the M-SPP model achieves the best detection accuracy with an mAP value of 82.28% when the number of parameters and the detection speed are relatively similar. This is sufficient to show that it can obtain local feature information of different sizes as well as global information, expand the sensory field, enrich the network expression, and effectively improve the network accuracy.  
The feature extraction network with a cavity convolution operation can solve the problem of feature resolution reduction due to the increase in the perceptual field. Different expansion rates in the ASPP structure can process the input feature maps in parallel and extract multi-scale target information. However, since the expansion rates (6, 12, and 18) do not fully utilize the perceptual field to extract effective information, the E-ASPP module is proposed in this paper to enhance the model effect, and the results are shown in Table 4. Compared with the original ASPP, the ASPPs can improve the detection accuracy up to 82.06%, which means it can perform feature extraction more effectively. Moreover, with the subsequent addition of the ECA attention mechanism to re-integrate the feature information, the mAP of the proposed E-ASPP was further improved to 82.21%, which is 0.15% higher in comparison to ASPPs.  
For comparing the effects of the three improvement strategies, M-SPP, E-ASPP, and CA, on the Center model, experiments were carried out, and the results are shown in Table 5. The mAP value of the original model was 79.64%. After taking the above strategies, the mAP values were all improved compared to the original model. When combining the three improvement strategies, the mAP value is improved to 83.42%, which is 3.78% higher than the original model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ASPP** & **ASPPs** & **ECA** & **Params [MB]** & **Speed [FPS]** & **mAP [\%]** \\ \hline \hline \multirow{2}{*}{\(\bigvee\)} & & & 32.95 & 26.22 & 81.78 \\  & & & 33.46 & 21.06 & 82.06 \\  & & \(\bigvee\) & 33.46 & 20.99 & 82.21 \\ \hline \hline \end{tabular} Note: \(\bigvee\) means with.  
\end{table}
Table 4: Characteristic pyramid E-ASPP.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**MP-SPP** & **E-ASPP** & **CA** & **Params [MB]** & **Speed [FPS]** & **mAP [\%]** \\ \hline \hline \multirow{2}{*}{\(\bigvee\)} & & & & 32.67 & 31.40 & 79.64 \\  & & & & 34.95 & 28.49 & 82.28 \\  & & & & 33.46 & 20.99 & 82.21 \\  & & & \(\bigvee\) & 32.67 & 29.75 & 81.27 \\  & & & \(\bigvee\) & 35.75 & 18.88 & 83.42 \\ \hline \hline \end{tabular} Note: \(\bigvee\) means with.  
\end{table}
Table 5: Center ablation experiment.  
To verify the detection effect of the Center model, YoLov5, YoLov7, Faster R-CNN, SSD, and CenterNet models are adopted for comparison, and the models have been converged when the same training parameters are used. The mAP values were calculated for all categories in the VOC2012 dataset after detection, and the results are shown in Table 6.  
For the YoLo family of algorithms, the Center model outperforms YoLov5 and YoLov7 in terms of mAP values, Speed, and Params. The faster R-CNN has the slowest detection speed of 10.50 FPS due to the shortcomings of two-stage detection. The SSD, in contrast, has the fastest detection speed but has the lowest detection accuracy 78.59%.