VISUAL ARTIFICIAL INTELLIGENCE-BASED AIRPORT TERMINAL SERVICE QUALITY MONITORING SYSTEM

• Main Author: Aris Hartadi, Ilham
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/71385
• Institution: Institut Teknologi Bandung

Airport operators may keep their service quality up above the ground by ensuring passengers and visitors comfort. Passengers comfort may comes in form of airport’s terminal crowd level, especially in peak-times. Surveillance camera installed at terminals can be utilized to monitor passengers crowd level with the help of visual artificial intelligence. This final research project presents a development of a dashboard to monitor terminal crowd level using video or image fed by surveillance cameras. These video and image feeds will then be analyzed by object detection model and algorithm. The dashboard with YOLOv5 algorithm and YOLOv5m-crowd-human pre-trained model was able to detect and monitor the passengers number and terminal crowd level, also present the result in forms of color indicator, passengers number and delta from previous number, and historical graph. This developed dashboard showed potential implementation to help airport operators keeping airport service quality and to help passengers getting the best experiences.