StairNet: A Computer Vision Dataset for Stair Recognition

Vision is important for transitions between different locomotor controllers (e.g., level-ground walking to stair ascent) by sensing the environment prior to physical interactions. Here we developed StairNet to support the development and comparison of deep learning models for visual recognition of stairs. The dataset builds on ExoNet – the largest open-source dataset of egocentric images of real-world walking environments. StairNet contains ~515,000 labelled images from six of the twelve original ExoNet classes. Images were reclassified using new definitions to increase the accuracy of the cutoff points between classes. The dataset was manually parsed several times during annotation to reduce misclassification errors and remove images with large obstructions. StairNet can support the development of next-generation deep learning models for visual perception and environment-adaptive control. Reference:1. Kuzmenko D, Tsepa O, Kurbis AG, Mihailidis A, and Laschowski B. (2023). Efficient visual perception of human-robot walking environments using semi-supervised learning. IEEE International Conference on Intelligent Robots and Systems (IROS). DOI: 10.1109/IROS55552.2023.10341654. 2. Kurbis AG, Mihailidis A, and Laschowski B. (2024). Development and mobile deployment of a stair recognition system for human-robot locomotion. IEEE Transactions on Medical Robotics and Bionics. DOI: 10.1109/TMRB.2024.3349602. 3. Ivanyuk-Skulskiy B, Kurbis AG, Mihailidis A, and Laschowski B. (2024). Sequential image classification of human-robot walking environments using temporal neural networks. IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob). DOI: 10.1109/BioRob60516.2024.10719798

视觉感知对于不同运动控制器之间的转换至关重要（例如，从平地行走至楼梯攀登），因为它能在物理交互之前感知环境。在本研究中，我们开发了StairNet，以支持视觉识别楼梯的深度学习模型的开发与比较。该数据集以ExoNet为基础——这是迄今为止最大的开源真实世界行走环境自摄图像数据集。StairNet包含了约51.5万张来自原始ExoNet十二个类别中的六个类别的标注图像。通过对图像进行重新分类，并采用新的定义以提高类别截止点的准确性。在标注过程中，数据集被多次手动解析，以减少误分类错误并移除带有较大障碍物的图像。StairNet能够支持下一代深度学习模型在视觉感知和环境自适应控制方面的开发。参考文献：1. Kuzmenko D, Tsepa O, Kurbis AG, Mihailidis A, and Laschowski B. (2023). Efficient visual perception of human-robot walking environments using semi-supervised learning. IEEE International Conference on Intelligent Robots and Systems (IROS). DOI: 10.1109/IROS55552.2023.10341654. 2. Kurbis AG, Mihailidis A, and Laschowski B. (2024). Development and mobile deployment of a stair recognition system for human-robot locomotion. IEEE Transactions on Medical Robotics and Bionics. DOI: 10.1109/TMRB.2024.3349602. 3. Ivanyuk-Skulskiy B, Kurbis AG, Mihailidis A, and Laschowski B. (2024). Sequential image classification of human-robot walking environments using temporal neural networks. IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob). DOI: 10.1109/BioRob60516.2024.10719798

搜集汇总

背景与挑战

背景概述

StairNet是一个专注于楼梯识别的计算机视觉数据集，包含约51.5万张从ExoNet数据集中精选并重新标注的图像，覆盖6个环境类别。该数据集通过精细的标注流程提高了分类准确性，旨在支持深度学习模型开发，用于人机行走环境中的视觉感知和自适应控制。