Road damage can pose significant economic, safety, and environmental challenges. The proposed solution uses advanced image processing techniques, machine learning algorithms, and statistical analysis to accurately identify different types of road damage such as cracks, potholes, and surface deterioration. From an economic point of view, detecting road damage is very important to enable timely repairs and maintenance. Poor road conditions can lead to higher vehicle maintenance costs, lower fuel efficiency, and higher transportation costs. By detecting damage early, transportation authorities can prioritize repairs, allocate resources effectively, and minimize the financial burden on individuals and businesses. From a safety perspective, road damage detection plays an important role in preventing accidents and injuries. Potholes and cracks can cause tire blowouts, loss of vehicle control, and crashes. Timely detection and repair of such damage reduces the risk of accidents and improves traffic safety for drivers, pedestrians, and cyclists. In addition, efficient road damage detection can also benefit the environment. Well-maintained roads reduce fuel consumption and greenhouse gas emissions from congestion associated with long-term repairs. By detecting damage early, the system will enable authorities to plan and implement remediation strategies that minimize environmental impact and contribute to sustainable transport infrastructure.
To overcome these challenges, many transportation companies are turning to artificial intelligence (AI) to improve the efficiency and effectiveness of road damage detection. Using AI algorithms and machine learning techniques like YOLO, to automatically detect and classify various types of road damage with high accuracy. This reduces the time and resources required to identify road damage, improving road safety and vehicle reliability.