# DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles
## 4 Experiments and Results
### Algorithm Performance Analysis  
Figures 6 and 7 present comparative precision-recall (P-R) evaluations across BDD100K and KITTI datasets, visualizing per-class detection characteristics at mAP@50 thresholds for both baseline YOLOv8s and our enhanced DGSS-YOLOv8s. Visually, the P-R curves for DGSS-YOLOv8s consistently demonstrate a better precision-recall trade-off, positioned generally higher and further to the right compared to the baseline curves across both datasets, indicating improved performance across various operating points. On BDD100K (Figure 6), while performance varies across classes, the most notable visual improvement is seen for the 'Pedestrian' category, where the curve for DGSS-YOLOv8s (see Figure 6b) is significantly elevated compared to YOLOv8s (see Figure 6a), visually underscoring the enhanced capability to reliably detect this challenging category while maintaining precision. On the KITTI dataset (Figure 7), this trend is even more pronounced for both 'Pedestrian' and 'Cyclist', with their P-R curves for DGSS-YOLOv8s (see Figure 7b) showing substantial upward shifts over the baseline (see Figure 7a), visually confirming enhanced detection reliability for these vulnerable road users. To further quantify the effectiveness of the proposed method, the results for per-class metrics are presented in Tables 4 and 5. Table 4 shows that, in the BDD100K dataset, DGSS-YOLOv8s improves mAP@50 and mAP@50:95 across all categories by 2.0% and 1.2%, respectively. Specifically, the mAP@50 for the'vehicle' class increased from 66.2% to 68.1%, while the mAP@50 for the 'traffic sign' and 'traffic light' classes increased by 0.7% and 1.5%, respectively. For the 'pedestrian' class, mAP@50 increased from 45.6% to 49.6%. Figure 6: P-R curve comparison on the BDD100K dataset. Table 5 indicates that in the KITTI dataset, DGSS-YOLOv8s achieved a 4.6% increase in mAP@50 and a 9.8% increase in mAP@50:95 across all categories. In particular, the \"Cyclist\" class saw its mAP@50 rise from 81.2% to 87.3%, while mAP@50:95 improved from 51.2% to 61.4%. The \"Pedestrian\" class experienced a 7.6% increase in mAP@50 and a 9.1% increase in mAP@50:95. These classes often involve objects that are more likely to be occluded and exhibit complex shapes in traffic scenarios. This demonstrates an overall improvement in model performance, particularly in detecting complex and occluded objects. To further evaluate the effectiveness of the proposed method, we conducted experiments comparing the results with various traditional object detection networks (Tables 6 and 7). \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**YOLOv8s**} & \multicolumn{5}{c}{**DGSS-YOLOv8s**} \\ \cline{2-9}  & **P** & **R** & **mAP@50** & **mAP@50:95** & **P** & **R** & **mAP@50** & **mAP@50:95** \\ \hline All & 61.1 & 44.2 & 48.5 & 22.1 & 65.2 & 45.7 & 50.5 & 23.3 \\ Vehicles & 66.3 & 62.3 & 66.2 & 38.4 & 69.9 & 63.2 & 68.1 & 40.4 \\ Pedestrian & 58.9 & 43.4 & 45.6 & 18.1 & 63.3 & 42.5 & 49.6 & 19.3 \\ Traffic Sign & 58.0 & 34.4 & 41.2 & 17.6 & 63.1 & 37.1 & 41.9 & 19.2 \\ Traffic Light & 61.3 & 36.8 & 41.1 & 14.0 & 64.6 & 39.9 & 42.6 & 14.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics of YOLOv8 and DGSS-YOLOv8s for every class on the BDD100K Dataset (%). \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**YOLOv8s**} & \multicolumn{5}{c}{**DGSS-YOLOv8s**} \\ \cline{2-9}  & **P** & **R** & **mAP@50** & **mAP@50:95** & **P** & **R** & **mAP@50** & **mAP@50:95** \\ \hline All & 90.1 & 75.9 & 82.3 & 55.6 & 89.2 & 80.5 & 86.9 & 65.4 \\ Car & 89.8 & 89.4 & 93.6 & 74.4 & 92.1 & 88.5 & 93.7 & 74.6 \\ Pedestrian & 91.6 & 63.9 & 72.1 & 41.2 & 88.8 & 69.4 & 79.7 & 50.3 \\ Cyclist & 88.0 & 74.5 & 81.2 & 51.2 & 86.6 & 83.6 & 87.3 & 61.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics of YOLOv8 and DGSS-YOLOv8s for every class on the KITTI dataset (%).