ROAD DAMAGE DETECTION FOR ADVANCED DRIVER ASSISTANCE SYSTEM

Abstract

Road infrastructure plays an important role in ensuring road safety and efficiency, particularly with the growing integration of Advanced Driver Assistance Systems (ADAS) and intelligent vehicles. In order to reduce accidents and preserve driving comfort, accurate road surface condition detection and monitoring are important. The rapid deterioration of road surfaces due to cracks, potholes, and other structural damage poses significant risks to driving safety and incurs high maintenance costs. Traditional inspection techniques are labor-intensive and time-consuming, and also rely primarily on manual inspection. The aforementioned constraints underscore the growing demand for automated, accurate, and reliable road damage detection systems that operate efficiently in real-world conditions. The performance of vision based road analysis has improved recently due to advances in deep learning. How ever, many current methods still struggle with generalization and robustness across diverse environmental conditions, and they lack reliable metrics such as uncertainty estimation. Moreover, the majority of existing systems only concentrate on detec tion accuracy, lacking risk-based decision-making processes that are necessary for realistic ADAS implementation. Advanced Driver Assistance Systems (ADAS) rely heavily on road surface condi tion knowledge since different types of road damage have a direct impact on driving safety, passenger comfort, and vehicle stability. However, rather than offering a structured and reliable solution that can be applied in a range of difficult situations, current approaches for studying road damage usually focus on specific tasks like pothole recognition, crack segmentation, or damage categorization. In this thesis, a complete framework for intelligent road damage segmentation and detection with improved decision support and reliability is presented. A modified v YOLOv4-based detection framework is implemented to detect potholes in various road condition images with accuracy and in real time. The model offers efficient and reliable pothole localization with high inference speed while maintaining strong de tection accuracy. At the pixel level, an enhanced residual UNet-based architecture is proposedforaccuratepotholesegmentation,whichcanassessthefine-graineddetails of pothole regions that are crucial for detailed surface assessment and maintenance analysis. Additionally, the regions that affect detection judgments are highlighted by the Grad-CAM visualization, which is utilized to evaluate the model’s predictions. A further contribution of this work is the development of an uncertainty-aware deep learning model that tackles the problem of road crack segmentation. Road cracks are challenging to accurately segment because they are frequently thin, uneven, and visually similar to other surface patterns, including oil stains, textural differences, and shadows. To improve reliability, reliable decision-making in ADAS requires knowledge of model confidence in addition to localization performance, especially for complex damage patterns. Hence, to evaluate the reliability of model outputs, prediction uncertainty is explicitly quantified and connected with segmentation ac curacy in the context of road crack segmentation, where uncertainty is estimated using stochastic dropout inference. The final contribution of this thesis presents a framework for risk-aware multiclass pavement damage identification, aiming to bridge the gap between detection results and actionable decisions made by ADAS. The suggested system incorporates detec tion results with a risk evaluation process, in contrast to conventional methods that merely identify damage categories. An enhanced YOLOv11-Based architecture for unified multi-class road damage detection is proposed. This design can handle a variety of damage categories, including potholes, cracks, manhole covers, blurred or white road markings, and large-scale structural anomalies. Damage recognition is converted into actionable driving states by integrating a structured ADAS decision vi logic into the framework. Depending on the type of damage discovered and the anticipated severity, the system classifies road conditions into operational levels, such as normal, warning, and emergency. The study extends the proposed methods to monitor dirt and gravel roads, which are commonly found in rural regions, in addition to paved road networks. Extensive experiments on multiple datasets demonstrate that the proposed methods achieve state-of-the-art performance while being robust under a range of environ mental conditions. Collectively, these contributions give a strong foundation for reliable, effective, and accurate road damage detection, providing useful informa tion for preventative maintenance and raising the standard of safety in contemporary transportation networks.