ROAD DEFECT DETECTION USING ISTM AND RANDOM FOREST

Road infrastructure plays a critical role in transportation efficiency, economic development, and public safety. However, road defects such as potholes, cracks, and surface deformations significantly impact vehicle safety and increase maintenance costs. Traditional road inspection methods rely on manual surveys conducted by engineers or maintenance personnel, which are time-consuming, labor intensive, and prone to human error. Automated defect detection using computer vision and machine learning has emerged as a promising solution to address these challenges. This study proposes an intelligent road defect detection system that integrates Long Short-Term Memory (LSTM) networks with a Random Forest classifier to improve the accuracy and reliability of road surface condition monitoring. The system processes road surface images and sequential data captured through cameras or sensors to identify different types of road defects. LSTM networks are used to analyze sequential patterns and temporal dependencies in road surface data, enabling the detection of progressive deterioration over time. Random Forest algorithms are utilized for efficient classification of extracted features and defect categories such as cracks, potholes, and surface irregularities. The hybrid approach combines temporal learning capabilities with robust classification techniques, improving detection accuracy and reducing false predictions. Feature extraction techniques are applied to capture texture, shape, and intensity variations in road images before feeding them into the classification models. Experimental results demonstrate that the proposed hybrid model achieves higher accuracy and reliability compared with traditional imageprocessing methods and single-model approaches. The system supports early identification of road defects and provides valuable insights for proactive road maintenance. Ultimately, the proposed framework contributes to the development of smart transportation infrastructure by enabling automated, scalable, and cost-effective road condition monitoring systems.