# DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles
## 4 Experiments and Results
### Visualizations  
To better understand the detection properties of DGSS-YOLOv8s, we provide several visualizations, including object detection results and gradient-based class activation mapping (Grad-CAM) [67].  
1. Object detection results: Figures 8 and 9 present the detection result comparison between YOLOv8s and DGSS-YOLOv8s on the BDD100K and KITTI test datasets, respectively. Compared to YOLOv8s, DGSS-YOLOv8s demonstrates improved target localization in complex backgrounds, significantly reducing both false detections and misidentifications. Furthermore, DGSS-YOLOv8s can detect targets that YOLOv8s misses, including pedestrians in challenging backgrounds, occluded cyclists, and distant pedestrians. This demonstrates that DGSS-YOLOv8s offers enhanced performance in recognizing complex shapes, small objects, and occluded targets compared to YOLOv8s. However, it is important to note that DGSS-YOLOv8s cannot completely eliminate false positives and false negatives.
2. Grad-CAM Results: We utilized Grad-CAM [67] to visualize CNN decision-making mechanisms, enabling the distinguishing and pinpointing of critical image regions influencing prediction outcomes. This technique constructs coarse localization maps by analyzing gradient propagation from the final convolutional layer to object-related concepts. As demonstrated in Figures 10 and 11, comparative analysis reveals DGSS-YOLOv8s' superior attention allocation compared to the baseline model. Visualization outcomes distinctly indicate YOLOv8s' inadequate focus on traffic elements, whereas our enhanced architecture exhibits optimized feature concentration--effectively reducing boundary ambiguities, emphasizing target-centric characteristics, and filtering irrelevant background data. Notably, in occlusion-intensive scenarios, the modified framework demonstrates improved detection consistency through better spatial reasoning. For small object detection tasks, DGSS-YOLOv8s successfully identifies and amplifies discriminative features, achieving precise characteristic extraction that translates to measurable accuracy improvements in complex traffic environments.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **IoU** & **Area** & **maxDets** & \begin{tabular}{c} **YOLOv8s** \\ **(BDD100K)** \\ \end{tabular} & \begin{tabular}{c} **DGSS-** \\ **YOLOv8s** \\ **(BDD100K)** \\ \end{tabular} & \
\begin{tabular}{c} **YOLOv8s** \\ **(KITTI)** \\ \end{tabular} &
\begin{tabular}{c} **DGSS-** \\ **YOLOv8s** \\ **(KITTI)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Average Precision (\%)} & 0.50:0.95 & all & 100 & 21.7 & 22.7 & 49.8 & 57.1 \\  & 0.50 & all & 100 & 47.3 & 49.3 & 79.8 & 86.1 \\  & 0.75 & all & 100 & 16.9 & 17.4 & 54.0 & 64.1 \\  & 0.50:0.95 & small & 100 & 10.7 & 11.4 & 25.6 & 35.3 \\  & 0.50:0.95 & medium & 100 & 35.3 & 36.8 & 55.2 & 60.4 \\ \hline \multirow{4}{*}{Average Recall (\%)} & 0.50:0.95 & all & 1 & 6.5 & 6.8 & 25.3 & 30.2 \\  & 0.50:0.95 & all & 10 & 26.2 & 26.9 & 54.0 & 61.4 \\ \cline{1-1}  & 0.50:0.95 & all & 100 & 33.2 & 34.6 & 57.1 & 63.1 \\ \cline{1-1}  & 0.50:0.95 & small & 100 & 22.0 & 23.6 & 38.2 & 44.5 \\ \cline{1-1}  & 0.50:0.95 & medium & 100 & 49.8 & 50.7 & 61.4 & 66.8 \\ \cline{1-1}  & 0.50:0.95 & large & 100 & 58.8 & 59.9 & 70.1 & 79.9 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance completion of YOLOv8s and DGSS-YOLOv8s algorithms for object detection of various sizes in BDD100K and KITTI.  
Figure 8: Detection results of YOLOv8s and DOSS-YOLOv8s on BDD100K outputs.  
Figure 9: Detection results of YOLOv8s and DOSS-YOLOv8s on KITTI outputs.