Airplanes are regularly inspected for any external damage between flights and during maintenance, especially when aircrew report possible lightning strike. Even today, the inspection is mainly done visually by authorized ground staff to look for evidence of possible damage, such as cracks and burn marks, etc. The process is not only inefficient and with poor traceability, but also troublesome when there is a need to inspect upper parts of the airframe. Approaches are available to automate the image acquisition, such as mounting a camera onto a moving robot and take multiple shots to cover the whole airplane. However, the acquired images still need to be screened thoroughly by technicians, which becomes an obstacle to automate the visual inspection process. The main reason for needing human intervention is the large number of distractions in the form of other features on the aircraft, not to mention the clutter produced by aircraft company livery. In this paper, novel methods to analyze the two-dimensional (2D) images and identify evidence of possible damage are presented. The methods are based on autocorrelation function (ACF) which is mostly used for fabric analysis. A pre-processing is firstly applied on the airframe image to remove background and enhance its quality. ACF is then implemented to look for abrupt changes which might be indications of damages. Lastly, a post-processing step is taken to filter out possible distractions. The proposed methods can work efficiently in various scenarios, which enables the possibility of automating the aircraft visual inspection process.