AI Car with Real Time Detection of Damaed Road and Lane Detection

The primary goal of this project is to develop a robust and efficient road damage detection and warning system using deep learning techniques. The system aims to provide real-time or near-real-time warnings to enhance traffic safety and reduce the risk of accidents. By eliminating the need for cloud-based processing and leveraging deep learning models, the project seeks to overcome the limitations of current systems, such as latency and the requirement for large labeled datasets. Additionally, the system aims to continuously map road conditions and provide valuable data to city planners and road maintenance authorities, facilitating proactive maintenance and repair scheduling. Ultimately, this project aims to improve road infrastructure quality and ensure safer travel for all road users. This study proposes a deep learning approach for road damage detection and warning, aimed at overcoming these limitations. By eliminating the dependence on cloud-based processing, our system can deliver real-time or near-real-time warnings, facilitating timely interventions to mitigate risks. Deep learning models have proven to be highly effective in image and video analysis tasks, making them well-suited for detecting road damage.Our vision-based lane detection approach is capable of operating in real-time with robustness to varying road conditions, lighting changes, and shadows. This enhances the safety and performance of autonomous driving systems. Additionally, the continuous mapping of road conditions generates valuable data for city planners and road maintenance authorities. Integration with cloud-based infrastructure enables the reporting of damage locations, allowing for proactive road maintenance and repair scheduling. By reducing the likelihood of accidents and improving the quality of road infrastructure, this AI-powered system significantly contributes to road safety and maintenance efficiency.