## 4 Experimental Results
### Ablation Study  
#### 4.3.1 Environmental Perception Attention (EPA)  
In environmental perception attention, the convolution kernel size (denoted by k) of a one-dimensional convolution represents, to some extent, the scope of contextual information captured in the spatial dimension--that is, how large the information in the receptive field can be obtained. However, intuitively, due to the saturation effect of the receptive field, blindly increasing the convolution kernel cannot continuously increase the receptive field of the model. In addition, all pixels in the receptive field contribute differently to the response of the model output, and the effective receptive field only accounts for a small part of the theoretical receptive field [54]. We explored the impact of EPA with different convolution kernel sizes on the performance of the detection algorithm, and the results are shown in Table 2.  
From the overall perspective of Table 2, after increasing k, mAP is effectively improved within a certain range, which shows that appropriately increasing the convolution kernel size can help the model detect the target better. However, when k continues to increase (such as from 17 to 23), the performance does not continue to improve, and even drops in some scenarios. We believe that this is because a larger k expands the receptive field, but at the same time may introduce more irrelevant background information, causing the model to confuse the target and the background, increasing false detections. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Backbone** & **Input Size** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS] (\%)** & **Params (M)** & **Flops (G)** \\ \hline Faster-RCNN & ResNet-50 & 800 \(\times\) 800 & 50.4 & 28.2 & 41.389 & 86.1 \\ SSD & VGG-16 & 800 \(\times\) 800 & 48.7 & 24.0 & 25.566 & 88.6 \\ TOOD & ResNet-50 & 800 \(\times\) 800 & 57.0 & 31.4 & 32.037 & 74.1 \\ VFNet & ResNet-50 & 800 \(\times\) 800 & 54.5 & 29.6 & 32.728 & 70.9 \\ YOLOv3-tiny & Darknet53 & 640 \(\times\) 640 & 40.4 & 22.3 & 12.136 & 19.1 \\ YOLOv5s & CSPDarknet53+SPPF & 640 \(\times\) 640 & 59.1 & 33.7 & 9.153 & 24.2 \\ YOLOv10s & CSPDarknet53+SPPF & 640 \(\times\) 640 & 58.8 & 33.9 & 8.042 & 24.5 \\ YOLO11s & CSPDarknet53+SPPF & 640 \(\times\) 640 & 59.7 & 33.9 & 9.416 & 21.3 \\ Ours & CSPDarknet53+SPPF & 640 \(\times\) 640 & 60.9 & 34.9 & 11.193 & 30.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance of our method and other models on the BDD100K dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Setting** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** & **Params (M)** & **Flops (G)** \\ \hline Baseline & 59.6 & 34.0 & 11.139 & 28.7 \\ +EPA (k = 5) & 60.1 & 34.5 & 11.152 & 28.7 \\ +EPA (k = 7) & 60.0 & 34.4 & 11.156 & 28.8 \\ +EPA (k = 9) & 60.2 & 34.5 & 11.160 & 28.8 \\ +EPA (k = 17) & 60.5 & 34.6 & 11.175 & 28.9 \\ +EPA (k = 19) & 60.2 & 34.6 & 11.179 & 28.9 \\ +EPA (k = 21) & 60.2 & 34.5 & 11.183 & 28.9 \\ +EPA (k = 23) & 60.1 & 34.4 & 11.187 & 29.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effect of varying k in environmental perception attention on model performance. To analyze the above phenomenon, we calculated and quantified the high contribution area ratio r of the minimum rectangle under different contribution score thresholds t by the method in RepLKNet [12] to show the effective receptive field (ERF) size of the model. The results are shown in Table 3. Specifically, the percentage t of the pixel contribution score in the model output feature map is provided by the rectangular area located in the center, and the ratio of this area to the overall area is r, which shows the ratio of the contribution of pixels in the central area of different sizes to the overall area. Under the same score threshold t, the larger the area ratio r, the more uniform the distribution of high contribution pixels and the larger the effective receptive field.