## 4 Experimental Results
### Evaluating the Detection Results of the Model  
To assess the object detection performance of the proposed model, IDP-YOLOV9, across diverse adverse weather conditions, comprehensive comparative experiments were conducted on the same test dataset, employing both cross-sectional and longitudinal analyses. Initially, the IDP-YOLOV9 algorithm was pitted against leading CNN object detectors such as Faster R-CNN, YOLOV8, YOLOV9, and so on. Table 3 showcases the detection outcomes of these different detectors across varying fog concentrations. Notably,  
Figure 5: The entire process of the IDP + YOLOV9 network, including (**a**) defogging processes, (**b**) deraining processes, and (**c**) desnowing processes.  
the results indicate that, across multiple adverse weather conditions, the object detection accuracy achieved by IDP-YOLOV9 outperforms the aforementioned algorithms.  
To validate the impact of image restoration on subsequent object detection algorithms, comparative experiments were conducted on object detection using different algorithms under various adverse weather conditions, including AOD-NET (defogging), GridDehazeNet (defogging), Efficient-DeRain (deraining), ADMMResNet (deraining), UDenseNet (desnowing), and All In One (desnowing). Figure 6 showcases the outcomes of different snow removal models. The figure illustrates that in snowy weather conditions, the IDP-YOLOV9 algorithm not only effectively detects and processes the weather but also achieves notably higher accuracy and lower miss detection rates compared to other algorithms. The integration of image processing and enhancement within a parallel architecture, along with the jointly optimized YOLOV9 algorithm proposed in this research, significantly enhances performance across diverse conditions. Furthermore, the jointly optimized model demonstrates superior performance compared to the methods mentioned previously, achieving higher average precision across diverse adverse weather conditions (fog, rain, and snow).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & **Faster** & \multirow{2}{*}{**SSD**} & **RetinaNet** & **YOLOV8** & **YOLOV9** &
\begin{tabular}{c} **IDP-YOLOV9** \\ **(Ours)** \\ \end{tabular} \\ \hline fog & 0.517 & 0.525 & 0.535 & 0.587 & 0.608 & 0.674 \\ rain & 0.524 & 0.535 & 0.545 & 0.596 & 0.624 & 0.691 \\ snow & 0.514 & 0.529 & 0.538 & 0.588 & 0.638 & 0.704 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average mAP for different object detection methods under various adverse weather conditions.  
Figure 6: The processing and detection effects of different algorithms on power scene images from drones’ perspective in snowy weather: (**a**) U-DenseNet algorithm; (**b**) All In One algorithm; (**c**) IDP-YOLOV9.  
The method proposed in this paper exhibits certain advantages in the image processing and detection of power construction site images under various adverse weather conditions. Due to the difficulty in obtaining paired adverse weather images of the same scene, the method proposed in this paper was trained using conventional datasets, including synthetic datasets. Nevertheless, the proposed method is applicable to real-world power construction site environments under diverse adverse weather conditions.