Abstract:

The identification of road damage using automated systems is very necessary for ensuring the continued safety and durability of transportation infrastructure. Traditional techniques for evaluating road damage sometimes entail physical inspections and procedures that require a lot of manual effort. These approaches may be time-consuming and might lead to errors caused by human intervention. The purpose of this research is to investigate a novel method for detecting road damage by using Unmanned Aerial Vehicles (UAVs) in conjunction with deep learning methods. In order to get comprehensive aerial photography of road surfaces, the proposed system makes use of unmanned aerial vehicles (UAVs) that are fitted with high-resolution cameras. Cracks, potholes, and surface wear are some of the forms of road damage that are identified and classified using sophisticated deep learning techniques, notably convolutional neural networks (CNNs). These photos are then processed using these algorithms to detect and categorize the different types of road damage. The deep learning model is trained on a vast dataset of road photos that have been tagged, which enables it to

Description:

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