With rapid advancements in machine vision technology，various aviation safety agencies，research organizations，and leading companies are actively engaged in the development，test flights，and certification of aircraft Visual Landing System (VLS).Firstly，the basic working principle and operation concepts of VLS are investigated，and an airport runway detection model based on the YOLOv5 network architecture is proposed and simulated on the Landing Approach Runway Detection (LARD) dataset.Furthermore，all the VLS functions including runway detection are listed.On the basis of considering the uncertainty and generalization gap of machine learning model，the failure conditions of each function are identified and categorized according to the severity of their impacts，and the means of compliance used to verify the safety requirements related to the failure conditions is given.It is shown that the precision，recall，and mAP_0.5 of the proposed model reaches 95%，96%，and 98% respectively，the results of functional hazard assessment provide ideas and references for the development of VLS，the functional definition of AI-based avionics system and the analysis of the failure conditions.