Pavement Crack Detection

The proposed deep learning-based model seeks to automate the detection of cracks and potholes in high-resolution aerial imagery obtained through drone cameras. As stated above, road surfaces degrade in terms of their physical condition due to many factors such as the weight of vehicles on the road, constant movement of vehicles, substandard construction of roads, water seepage, extreme weather, and temperature changes. When pavement deterioration is not detected and repaired in good time, small damages can grow into bigger structural problems, thereby raising the cost of maintenance work and posing significant threats to the safety of drivers, pedestrians, and other road users.

Conventional road inspection processes mainly rely on manual inspections performed by field officers and specialized inspection tools. Even though they provide reliable results, these methods often take much time and effort and, therefore, can be resource-intensive, especially when inspecting large road networks. It may also become challenging for authorities to conduct inspections regularly due to manual methods of collecting data.

The use of deep learning and computer vision in the proposed pavement inspection model allows for automated detection of defects in road surfaces through analysis of high-resolution aerial images that have been captured through drones. The use of deep learning and computer vision helps to detect cracks and potholes in surveyed roads, thereby making it possible for the model to turn drone imagery into useful information for road infrastructure.

In addition, an aerial inspection utilizing drones offers a flexible solution for road, highway, street, parking lot, and bridge inspection, and any other type of transportation infrastructure. In addition, high-resolution imagery enables the collection of surface-level data, which might be hard to gather otherwise by means of conventional ground surveys. After processing images in the detection model, identified cracks and potholes can be mapped, evaluated, and incorporated into GIS-based maintenance processes.

The model can help to optimize road maintenance practices for various groups, including, but not limited to, road maintenance departments, transportation departments, municipal authorities, infrastructure managers, engineering companies, and GIS specialists. The identification of damaged areas and provision of relevant information about the road condition will allow for planning inspections, scheduling maintenance efforts, monitoring deterioration, and addressing cracks and potholes before it costs even more to fix.

All things considered, automated detection of cracks and potholes on road surfaces based on drone imagery, computer vision, machine learning, and geospatial analysis is an efficient solution for road monitoring in the modern world. The use of AI-based inspections can enhance existing processes of infrastructure maintenance and enable smarter, data-driven decision-making.