© 2020 IEEE.While airplanes are waiting in the apron, some routines, such as fuel, catering and cargo loading, must be performed before take-off and after landing. During this period images of the airplane in the apron are continuously recorded and evaluated in order to monitor whether these operations are carried out in accordance with the rules without any security problem. Information from different systems and visual evaluations are combined to determine the time, duration and type of operations for each airplane, and then being evaluated for safety and effectiveness of time usage. The aim of this paper is to provide an infrastructure for automatic analysis of aforementioned metric values of an airplane staying in apron. This is done by designing and implementing deep learning instance segmentation algorithm with Mask R-CNN, which is based on detecting the airplane in the video images and by pointing out the components of airplane such as front, back, tail, and gates of the airplane.