A Large-Scale Dataset for Benchmarking Elevator Button Segmentation and Character Recognition
Refereed conference paper presented and published in conference proceedings

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AbstractHuman activities are hugely restricted by COVID-19, recently. Robots that can conduct inter-floor navigation attract much public attention since they can substitute human workers to conduct the service work. However, current robots either depend on human assistance or elevator retrofitting, and fully autonomous inter-floor navigation is still not available. As the very first step of inter-floor navigation, elevator button segmentation and recognition hold an important position. Therefore, we release the first large-scale publicly available elevator panel dataset in this work, containing 3,718 panel images with 35,100 button labels, to facilitate more powerful algorithms on autonomous elevator operation. Together with the dataset, a number of deep learning based implementations for button segmentation and recognition are also released to benchmark future methods in the community.
Acceptance Date28/02/2021
All Author(s) ListJianbang Liu, Yuqi Fang, Delong Zhu, Nachuan Ma, Jin Pan, Max Q.-H. Meng
Name of ConferenceInternational Conference on Robotics and Automation
Start Date of Conference31/05/2021
End Date of Conference04/06/2021
Place of ConferenceXi`an
Country/Region of ConferenceChina
LanguagesEnglish-United States

Last updated on 2022-09-02 at 15:48