Autonomous Removal of Perspective Distortion of Elevator Button Images based on Corner Detection

TL;DR

This paper introduces a deep learning method that detects button corners and corrects perspective distortions in elevator button images, improving recognition accuracy under challenging conditions.

Contribution

It presents a novel combination of segmentation, corner detection, and camera motion estimation to autonomously correct perspective distortions in elevator button images.

Findings

High accuracy in removing perspective distortions

Effective corner detection using the proposed model

Robustness across different viewing angles

Abstract

Elevator button recognition is a critical function to realize the autonomous operation of elevators. However, challenging image conditions and various image distortions make it difficult to recognize buttons accurately. To fill this gap, we propose a novel deep learning-based approach, which aims to autonomously correct perspective distortions of elevator button images based on button corner detection results. First, we leverage a novel image segmentation model and the Hough Transform method to obtain button segmentation and button corner detection results. Then, pixel coordinates of standard button corners are used as reference features to estimate camera motions for correcting perspective distortions. Fifteen elevator button images are captured from different angles of view as the dataset. The experimental results demonstrate that our proposed approach is capable of estimating camera motions and removing perspective distortions of elevator button images with high accuracy.