Zenboパトロール：違法駐車をリアルタイム認識・通知するマルチモーダル深層学習ベースの社会支援ロボット
Zenbo Patrol: A Social Assistive Robot Based on Multimodal Deep Learning for Real-time Illegal Parking Recognition and Notification
社会支援ロボットが駐車場を巡回し、GPT-4oによるマルチモーダル認識でナンバープレートを読み取り、違法駐車を検出すると管理者にLINEで即時通知するシステムを構築・検証した。
著者: Jian-jie Zheng, Chih-kai Yang, Po-han Chen, Lyn Chao-ling Chen
分類: cs.RO
原文アブストラクト
In the study, the social robot act as a patrol to recognize and notify illegal parking in real-time. Dual-model pipeline method and large multimodal model were compared, and the GPT-4o multimodal model was adopted in license plate recognition without preprocessing. For moving smoothly on a flat ground, the robot navigated in a simulated parking lot in the experiments. The robot changes angle view of the camera automatically to capture the images around with the format of license plate number. From the captured images of the robot, the numbers on the plate are recognized through the GPT-4o model, and identifies legality of the numbers. When an illegal parking is detected, the robot sends Line messages to the system manager immediately. The contribution of the work is that a novel multimodal deep learning method has validated with high accuracy in license plate recognition, and a social assistive robot is also provided for solving problems in a real scenario, and can be applied in an indoor parking lot.