The rapid deterioration of transportation infrastructure worldwide necessitates innovative approaches to pavement condition assessment and maintenance planning. Traditional manual inspection methods for pavement distress detection are labor-intensive, time-consuming, subjective, and often pose safety risks to inspection personnel. This research investigates the integration of Unmanned Aerial Vehicles (UAVs) with advanced computer vision technologies to develop an automated system for pavement distress detection and classification. The study presents a comprehensive framework that combines high-resolution aerial imagery acquisition through UAV platforms with state-of-the-art machine learning algorithms, including deep learning neural networks and computer vision techniques, to identify, classify, and quantify various types of pavement distresses such as cracks, potholes, rutting, and surface deterioration.
The proposed methodology employs a multi-stage approach beginning with UAV-based data collection using high-resolution cameras and specialized imaging sensors. The captured aerial imagery undergoes preprocessing to enhance image quality and standardize lighting conditions. Subsequently, advanced computer vision algorithms, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and edge detection techniques, are applied to automatically identify and classify different types of pavement distresses. The system incorporates Geographic Information System (GIS) integration to provide spatial context and enable comprehensive condition mapping of road networks.
Experimental validation was conducted on multiple highway segments and urban road networks, demonstrating the system's effectiveness in detecting various pavement distress types with accuracy rates exceeding 85% for crack detection and 90% for pothole identification. The automated classification system successfully distinguished between different severity levels of pavement distresses, enabling prioritized maintenance scheduling. Comparative analysis with traditional ground-based inspection methods revealed significant improvements in inspection speed, coverage area, and data consistency while maintaining comparable accuracy levels.
The research findings indicate that UAV-integrated computer vision systems offer substantial advantages over conventional pavement inspection approaches, including enhanced safety for inspection personnel, reduced inspection time by up to 70%, improved data consistency and objectivity, and comprehensive coverage of large road networks. The system's ability to generate detailed condition reports with precise spatial coordinates facilitates efficient maintenance planning and resource allocation. Furthermore, the automated nature of the system enables frequent monitoring cycles, supporting proactive maintenance strategies that can extend pavement lifespan and reduce overall infrastructure costs.
The study also addresses challenges associated with UAV-based pavement inspection, including weather dependency, regulatory compliance, image quality variability, and computational requirements for real-time processing. Solutions and mitigation strategies are proposed to overcome these limitations, ensuring reliable system performance across diverse operational conditions. The research contributes to the advancement of intelligent transportation infrastructure management systems and provides a foundation for future developments in automated pavement condition assessment technologies.