A Large-Scale Dataset for Benchmarking Elevator Button Segmentation and Character Recognition
This addresses the need for fully autonomous inter-floor navigation in robots, which is currently limited by reliance on human assistance or elevator retrofitting, but it is incremental as it focuses on dataset creation rather than novel algorithmic breakthroughs.
The authors tackled the problem of autonomous elevator operation by releasing the first large-scale public dataset for elevator button segmentation and character recognition, containing 3,718 panel images and 35,100 button labels, and provided deep learning implementations to benchmark future methods.
Human 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. The dataset will be available at \url{https://github.com/zhudelong/elevator_button_recognition