## 4 Experiment
### Results and Analyses  
Table 7 shows that the gradual introduction of each functional module significantly improved the performance of the model. Replacing C2f with the C2f_Van module effectively reduced the model complexity, benefiting from the deep training strategy and the series-informed activation function. Although the use of DWConv resulted in a tiny loss in the FPS and accuracy, the analysis shows that its high memory access volume of caused delays, but this also brought greater benefits that can significantly reduce the model parameters. The performance of the model obtained by using WIoU\({}_{v3}\) loss module is shown in the bold part \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & \multirow{2}{*}{**FPS**} & **Parameters** & **FLOPs** & **Weights** \\  & **(\%)** & **(\%)** & & **(M)** & **(G)** & **(MB)** \\ \hline YOLOv8 & 98.3 & 69.0 & 43.82 & 11.13 & 28.4 & 22.5 \\ +C2f\_Van & 98.4 & 69.0 & 47.02 & 7.84 & 20.1 & 15.9 \\ +DWConv & 98.4 & 68.6 & 44.85 & 5.54 & 15.5 & 11.3 \\ +FasterDetect & 98.3 & 68.2 & 50.35 & 4.03 & 8.5 & 8.3 \\ +WIoU\({}_{v3}\) & 98.3 & **73.1** & **50.35** & **4.03** & **8.5** & **8.3** \\ Improvement & & (+4.1\%) & (+6.53) & (\(-63.81\%\)) & (\(-70.07\%\)) & (\(-63.11\%\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Gradual increase in ablation experiments with improved modules. Figure 8: Comparison of Box_Loss values of loss function. of Table 7. Compared with the YOLOv8 model, they have corresponding improvements, as shown in line 7 of Table 7. The experimental results show that the introduction of FasterDetect based on PConv further reduced the computational complexity, significantly decreased computational delays, and achieved an unprecedentedly high FPS of 50.35. Finally, the adoption of the dynamic non-monotonic FM in WIoU\({}_{x3}\) elevated the accuracy of the CGADNet to a higher level, reaching 73.1% in the [EMAIL_ADDRESS]:0.95. Based on the experiment dataset, we conducted horizontal comparative experiments using the MMDetection [73] framework, encompassing mainstream classic models such as Faster R-CNN [21], EfficientNet [74], MASK R-CNN [22], Swin Transformer [75], and DETR [34], as well as models like CDNet [55], YOLOv5 [26], YOLOv7 [27], and RT-DETR [76]. Excitingly, our improved model achieved significant advantages across all metrics, surpassing the classic and SOTA models. The comparative results are shown in Table 8. In Table 8, bold values on line 11 highlight the performance of our proposed model over other models. Specifically, we achieved remarkable results across multiple performance metrics, such as the mAP, parameters, FLOPs, and weight. This indicates that our optimization strategies not only successfully reduced the model size and lowered the resource demands, making it easier for edge deployment and use, but it also significantly improved the detection accuracy of pedestrian crossings and guiding arrows. The experimental results further confirm the exceptional performance of the proposed model in real-world crosswalk and guide arrow scenarios. Moreover, to better understand the proposed improvements, we further used Gradcam [77] and YOLO-LCD [78] for the feature visualization and model optimization, which is a visualization method used to interpret predictions made by deep learning models. It calculates the gradients of the target class for the output of the model and then maps these gradients back to the intermediate-layer feature maps of the model, generating attention maps highlighting the regions that the model focuses on during predictions. Figure 9 displays the visualization results of the CGADNet and YOLOv8 under different weather conditions for the detection performance. Red indicates high activation areas, while blue represents areas with weaker activation in the detection results. Compared with the baseline model, the proposed model had better attention maps on various samples.