# DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles
## 4 Experiments and Results
### Algorithm Performance Analysis
Figure 7: P-R curve comparison on the KITTI dataset. Comparative analysis against other detectors (Tables 6 and 7) highlights DGSS-YOLOv8s's strong performance. On BDD100K, DGSS-YOLOv8s (51.3% mAP@50, 103.1 FPS) demonstrated significant speed advantages (5.3-8.4x) over traditional two-stage detectors like Faster R-CNN [10] with comparable accuracy and substantially higher mAP@50 (+17.4%) than earlier single-stage models like SSD [12]. Against recent SOTA models (e.g., YOLOv9 [46], YOLOv10 [47]), it offered significantly higher FPS (7.9-11.6% faster) with competitive mAP@50. On KITTI, DGSS-YOLOv8s (86.9% mAP@50, 102.5 FPS) substantially outperformed traditional detectors (e.g., +7.4% mAP@50, 4.3 \(\times\) FPS vs. Faster R-CNN) and, notably, demonstrated both higher mAP@50 (+1.6% to +3.8%) and significantly higher FPS (7.3-12.4% faster) than YOLOv9 and YOLOv10. Overall, DGSS-YOLOv8s achieved a compelling balance between high FPS and accuracy for challenging objects (small, occluded, irregular) in complex traffic scenarios. While not always achieving the absolute highest mAP (e.g., vs. YOLOv9 on BDD100K), its consistent high FPS across datasets, coupled with substantial accuracy gains in targeted areas (per ablation and per-class results in Tables 2-5) underscores its novelty. This lies in the synergistic integration of modules, specifically enhancing the detection of difficult cases on an efficient baseline, which is crucial for practical autonomous driving deployment. Furthermore, DGSS-YOLOv8s showed enhanced multi-scale target detection capabilities (Table 8), assessed using COCO metrics (small: \(<\)32\({}^{2}\) pixels). For small objects, \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **mAP@50 (\%)** & **mAP@50:95 (\%)** & **FPS (f/s)** \\ \hline Faster-RCNN (ResNet50) & 62.7 & 55.1 & 51.5 & 25.7 & 19.4 \\ Cascade-RCNN (ResNet50) & 62.3 & 55.8 & 51.7 & 26.7 & 12.3 \\ RetinaNet (ResNet50) & 58.2 & 48.8 & 46.7 & 21.7 & 26.3 \\ SSD & 46.3 & 38.4 & 33.9 & 14.8 & 48.6 \\ YOLOv4 & 63.0 & 47.6 & 50.9 & 23.6 & 97.4 \\ YOLOv5s & 60.8 & 44.3 & 48.2 & 21.6 & 180.2 \\ YOLOv6s & 56.2 & 47.9 & 47.3 & 22.0 & 132.8 \\ YOLOv3s & 62.0 & 47.0 & 49.3 & 22.2 & 140.5 \\ YOLOv7-tiny & 59.1 & 42.2 & 44.7 & 18.8 & 225.7 \\ YOLOv8s & 61.5 & 44.7 & 49.1 & 22.3 & 125.6 \\ YOLOv9 & 67.4 & 48.5 & 53.8 & 26.1 & 92.4 \\ YOLOv10 & 66.1 & 47.2 & 51.5 & 25.3 & 96.3 \\ RT-DETR (ResNet-50) & 64.7 & 45.8 & 50.1 & 24.5 & 98.1 \\ DGSS-YOLOv8s & 65.2 & 46.1 & 51.3 & 24.0 & 103.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results comparing different algorithmic models on the BDD100K dataset. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **mAP@50 (\%)** & **mAP@50:95 (\%)** & **FPS (f/s)** \\ \hline Faster-RCNN (ResNet50) & 89.3 & 72.5 & 79.5 & 48.1 & 24.1 \\ Cascade-RCNN (ResNet50) & 75.7 & 63.2 & 66.9 & 35.8 & 35.4 \\ RetinaNet (ResNet50) & 87.4 & 75.8 & 78.8 & 48.7 & 17.6 \\ SSD & 69.6 & 59.4 & 62.0 & 31.3 & 56.3 \\ YOLOv4 & 91.6 & 78.4 & 84.2 & 56.3 & 97.5 \\ YOLOv5s & 92.0 & 73.8 & 82.1 & 51.5 & 230.7 \\ YOLOv6s & 87.6 & 72.5 & 80.2 & 50.9 & 155.2 \\ YOLOv3s & 93.1 & 74.3 & 83.2 & 53.8 & 168.3 \\ YOLOv7-tiny & 88.7 & 70.2 & 79.0 & 46.3 & 276.5 \\ YOLOv8s & 90.1 & 75.9 & 82.3 & 55.6 & 148.7 \\ YOLOv9 & 94.2 & 77.1 & 85.3 & 57.2 & 91.2 \\ YOLOv10 & 92.8 & 75.5 & 83.1 & 55.9 & 95.5 \\ RT-DETR (ResNet-50) & 91.5 & 74.3 & 81.0 & 54.2 & 97.4 \\ DGSS-YOLOv8s & 89.2 & 80.5 & 86.9 & 65.4 & 102.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental results comparing different algorithmic models on the KITTI dataset. AP-small reached 11.4% on BDD100K (+0.7% over YOLOv8s) and a significant 35.3% on KITTI (+9.7%). Corresponding AR-small improvements were also notable (+1.6% on BDD100K, +6.3% on KITTI), confirming robust detection efficacy for these critical targets.