# Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8
## 2 Related Works  
Integrating advanced technologies in vehicle stabilization on uneven terrains is crucial for enhancing safety and comfort. However, despite numerous innovations, significant scientific obstacles remain in this area. These challenges are rooted in the need for real-time and adaptive stability systems to continuously monitor the road for minor imperfections while minimizing excessive maintenance costs and performance issues across different weather conditions. The implementation of IoT for anomaly detection is widely observed across various fields, including image segmentation, pattern recognition, and network intrusion detection [19; 20]. In a broader context, traditional tasks in image processing, computer vision, and character recognition [21; 22] often leverage deep neural networks. However, the integration of IoT with real-time processing for advanced technologies demands a more robust and customized framework, such as _YOLO_ (You Only Look Once) [23]. The stability and usability of our approach to adapting YOLO are enhanced by incorporating our framework and hardware modifications. Table 2 compares our proposed system with existing works based on various implemented features. The capability for real-time vehicle balance adjustment has not been previously available; adding GY-521 sensors enhances this feature, which many prior systems have not incorporated [24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. By integrating YOLOv8, we design an intelligent safety system that uses predictive road hazard detection to warn the driver of approaching obstacles proactively. Furthermore, with these features, a servo motor system can automatically adjust the position of specific vehicle components in real time, a significant advancement over traditional stability systems. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Related Works** & **H** & **S** & **GY-521** & **YOLOv8** & **URD** & **SC** \\ \hline
[24] & No & Yes & No & No & Yes & Yes \\
[25] & Yes & No & No & No & Yes & Yes \\
[26] & Yes & No & No & No & Yes & Yes \\
[27] & Yes & Yes & No & No & No & Yes \\
[28] & No & Yes & No & No & Yes & Yes \\
[29] & Yes & Yes & No & No & Yes & No \\
[30] & No & Yes & No & No & Yes & Yes \\
[31] & Yes & Yes & No & No & Yes & Yes \\
[32] & Yes & Yes & No & No & No & Yes \\
[33] & Yes & No & No & No & Yes & Yes \\
**Our Approach** & **Yes** & **Yes** & **Yes** & **Yes** & **Yes** & **Yes** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison Between Recent Works and Our Proposed Approach. For instance, YOLOv8 can identify pavement deterioration, provide better road maintenance, and offer enhanced road maintenance. Yet, it does not address the need for a holistic approach that would permit real-time adjustments for the vehicle's stability depending on the terrain it is driving on. Our system uses a combination of GY-521 sensors, servo motors, and YOLOv8 in contrast to the traditional static response found in the literature, thus ensuring a dynamic and immediate correction for road defects. For instance, YOLOv8 can identify pavement deterioration, provide better road maintenance, and offer enhanced road maintenance. Yet, it does not address the need for a holistic approach that would permit real-time adjustments for the vehicle's stability depending on the terrain it is driving on [34]. Our system uses a combination of GY-521 sensors, servo motors, and YOLOv8 in contrast to the traditional static response found in the literature, thus ensuring a dynamic and immediate correction for road defects. Moreover, unlike previous approaches, our system consists of Uneven Roads Detection (URD) and Stabilize Car (SC), so its application goes through a comprehensive and scalable implementation. Most existing solutions struggle to adapt to dynamic terrain conditions, such as wet or icy roads, mountainous areas, and frequent road irregularities [35; 36].