Autonomous Removal of Perspective Distortion of Elevator Button Images based on Corner Detection
This work addresses the need for reliable elevator button recognition to enable autonomous elevator operation, though it is incremental with a small dataset.
The paper tackles the problem of accurately recognizing elevator buttons under challenging image conditions by proposing a deep learning-based approach to autonomously correct perspective distortions using button corner detection, achieving high accuracy in motion estimation and distortion removal as demonstrated on a dataset of 15 images.
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.