Automated Road damage detection is crucial for maintaining the safety and longevity of transportation infrastructure. Traditional methods for assessing road damage often involve manual inspections and labor-intensive processes that can be time-consuming and prone to human error. This study explores an innovative approach to road damage detection using Unmanned Aerial Vehicles (UAVs) combined with deep learning techniques. The proposed system utilizes UAVs equipped with high-resolution cameras to capture detailed aerial imagery of road surfaces. These images are then analyzed using advanced deep learning algorithms, specifically Convolutional Neural Networks (CNNs), to identify and classify various types of road damage, such as cracks, potholes, and surface wear. The deep learning model is trained on a large dataset of annotated road images, enabling it to learn complex patterns and features associated with different damage types. The research integrates several key components to enhance the accuracy and efficiency of damage detection. Data augmentation techniques are employed to increase the diversity of the training dataset, improving the model’s generalization capabilities across various road conditions and damage scenarios. Additionally, the study incorporates transfer learning, leveraging pre-trained CNN models to expedite the training process and enhance detection performance with limited data.
Manchalwar et al. (Sun,) studied this question.