# Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8
## 4 Dataset and Preprocessing
### Dataset Label Taxonomy and Configuration
## 6 Discussion and Limitations

### Experimental Limitations and Improvement Pathways

While the proposed system demonstrates strong multi-class detection performance and reliable vehicle stabilization, several limitations remain:

* Detection accuracy may degrade under extreme occlusion or poor lighting conditions. * Label ambiguity between visually similar classes (e.g., low ravelling vs. medium pothole) occasionally affects classification fidelity. * Real-time inference is currently optimized for Jetson Nano-class hardware, with support for ultra-low-power microcontrollers such as ESP32 still in development. To address these issues, future work will explore model compression techniques--including quantization, pruning, and knowledge distillation--to enable deployment on more constrained edge devices. Additionally, incorporating sensor fusion (e.g., combining visual data with inertial or infrared signals) can enhance detection robustness. We also envision implementing a policy-driven active learning pipeline, enabling the system to adapt its models post-deployment based on fleet-level feedback and evolving road conditions. ### Summary and Discussion

The proposed dual-layer stabilization framework effectively combines real-time road anomaly detection using YOLOv8 with motion feedback from the GY-521 sensor to adjust vehicle posture dynamically. Extensive testing across varied terrains--including rural, inclined, and uneven surfaces--demonstrates the system's ability to generalize well to real-world conditions. The model consistently achieves high precision and F1 scores across multiple classes, with a peak detection precision of 0.99 and robust generalization across unseen inputs. Confusion matrix analysis highlights areas where visual overlap between classes limits accuracy, suggesting future improvements via data augmentation or integration of additional sensor modalities. Overall, the results affirm the system's readiness for real-world prototyping in cost-sensitive or infrastructure-challenged settings. ## 7 Conclusions and Future Work

This paper presents a real-time, cost-effective system for dynamic vehicle stabilization on uneven roads, leveraging a GY-521 sensor and YOLOv8-based object detection. The framework dynamically compensates for terrain-induced instabilities through servo-actuated wheel adjustments and a dual-layer vehicle structure that isolates the passenger cabin from road vibrations. Experimental results on a physical testbed demonstrate robust performance across diverse road conditions--including potholes, bumps, and slopes--with stabilization effectiveness reaching up to 99%. By balancing high accuracy with low hardware requirements, the system is well-suited for deployment in resource-constrained environments. Broader Future Directions

Future development will optimize the system for ultra-low-power platforms, such as ESP32, using lightweight architectures like YOLOv8n and MobileNet. Additional robustness will be pursued via multi-modal inputs (e.g., depth, infrared), improving performance under adverse conditions such as occlusion or low light. We also plan to integrate active learning mechanisms that use edge- and cloud-sourced feedback from deployed vehicles to incrementally refine detection and control policies. Ultimately, this architecture may scale into a fleet-wide road monitoring platform for smart cities, enabling predictive maintenance, infrastructure planning, and safer autonomous vehicle navigation. **Author Contributions**: Methodology, A.A.M. ; validation, T.H. ; formal analysis, M.B. and A.A.M. ; resources, A.K.I.R. ; data curation, A.A.M. and T.H. ; writing--original draft, A.A.M., T.H., S.U. and M.N.A. ; supervision, M.B. and H.S. All authors have read and agreed to the published version of the manuscript. **Funding**: This research received no external funding. **Data Availability Statement**: All the related code and data for this research are available in the GitHub repository linked below.