# Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8
## 4 Dataset and Preprocessing
### Dataset Label Taxonomy and Configuration
\begin{table}
\begin{tabular}{l l} \hline \hline
**Attribute** & **Description** \\ \hline Dataset Source & Roboflow: Pothole Detection Dataset \\ Dataset Link & [[https://universe.roboflow.com/projects-hra06/potholedetection-cxudc/dataset/1](https://universe.roboflow.com/projects-hra06/potholedetection-cxudc/dataset/1)]([https://universe.roboflow.com/projects-hra06/potholedetection-cxudc/dataset/1](https://universe.roboflow.com/projects-hra06/potholedetection-cxudc/dataset/1)) (accessed on 28 May 2025) \\ Total Images & 2358 \\ Train/Val/Test Split & 1886/235/237 (80\%/10\%/10\%) \\ Image Resolution & Ranges from 416 \(\times\) 416 to 1024 \(\times\) 1024 pixels \\ Annotation Format & COCO-style bounding boxes \\ Object Classes & Pothole, Crack, Bump \\ Labeling Method & Manually annotated and reviewed \\ Terrain Types & Urban, rural, mountainous, snowy, wet roads \\ Collection Conditions & Varied lighting, weather, and angles \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of Public Dataset Used for YOLOv8 Road Anomaly Detection. Figure 6: Examples from Training Set with Label Overlays. Figure 7: Class Distribution and Object Location Heatmap. ## 5 Experimental Results and Analysis

To evaluate system performance comprehensively, we tested the detection and stabilization framework across diverse road types: smooth urban highways, uneven rural paths, twisting hilly terrains, and potentially slick coastal areas. These tests assess the system's ability to detect anomalies and maintain vehicle stability under variable surface types, traction conditions, and environmental influences. ### Extended Obstacle Evaluation and Generalization Tests

Our research conducted a comprehensive assessment of the vehicle's detection and stabilization system by physically creating a variety of real-world road anomalies. These anomalies included small and large bumps measuring 2.5 cm and 5.2 cm and potholes at 1.8 cm and 4.7 cm. We designed these obstacles using coarse gravel, asphalt patches, and textured foam to simulate rough terrain effectively. The physical challenges were systematically deployed on a controlled mock terrain testbed, allowing consistent trial validation. Our methodology rigorously tested each scenario with a complete hardware prototype that integrated a GY-521 sensor, an onboard camera, and a servo-controlled stabilization mechanism. To ensure the reliability and consistency of our results while minimizing bias from outliers, we conducted multiple independent trials. Notably, our findings are based solely on physical testing, eschewing reliance on synthetic or simulated data, reinforcing the results' ecological validity. We utilized the Blynk IoT dashboard to log real-time anomaly measurements during each trial to enhance transparency and reproducibility. This provided live telemetry data, confirming the heights of obstacles encountered by the system in each detection scenario and permitting thorough cross-verification of performance data (see Table 10). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Obstacle Type** & **Avg. Size (cm)** & **Detection Precision (\%)** & **Detection Recall (\%)** & **Tilt Reduction (\%)** & **Stabilization Success (\%)** \\ \hline Small Bump & 2.5 & 89.2 & 91.3 & 83.1 & 94.4 \\ Large Bump & 5.2 & 93.4 & 95.7 & 85.7 & 89.2 \\ Small Pothole & 1.8 & 86.5 & 88.0 & 79.0 & 95.9 \\ Large Pothole & 4.7 & 90.1 & 92.3 & 81.8 & 90.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Extended Anomaly Evaluation Results Across Scaled Obstacles (Physical Tests with IoT Validation). Figure 8: Size–Position Relationship Across Labels. These results confirm consistent detection and stabilization across increasingly difficult terrains, maintaining over 90% precision and average tilt correction above 80%. This validates the system's generalization ability despite being implemented on a resource-constrained prototype.