In recent times, the use of drones to monitor various types of transportation lines has attracted more attention. Unmanned aerial vehicles (UAVs) have a number of potential benefits over manual methods for inspecting transportation lines due to its permit scalable, quick, and affordable solutions to tasks that would otherwise be unsuitable for individuals who are subject to fatigue and measurement uncertainty. Therefore, the current study investigates the use of drones in image processing, early warning and situation assessment in the transportation sector. Due to their ability to capture aerial images in extremely high resolution at a low cost and while also covering large areas, drones are a very important source of visual data. The main goal of this work is to collect and analyse drone-shot images using MATLAB software in order to locate the line's fault and take the necessary corrective action to prevent accidents and save lives. The results of the current work concluded that the using of aerial image processing is very effective to increase and maximize the
capacities of the transportation lines. Moreover, it gives more safety for the lines. UAV can survey and take pictures of the railway line for a distance of about 8.4 km in one hour, whereas a worker would need a full day to cover the same distance (a worker scans a distance of 7 km per day), so it saved time and effort.