论文标题
基于深度倾斜的超快速楼梯检测
Deep Leaning-Based Ultra-Fast Stair Detection
论文作者
论文摘要
楼梯是城市环境中一些最常见的建筑结构。对于各种应用，楼梯检测是一项重要任务，包括对外骨骼机器人，人形机器人和救援机器人的环境感知以及视力障碍者的导航。大多数现有的楼梯检测算法很难处理楼梯结构材料的多样性，极度明亮和严重的遮挡。受人类感知的启发，我们提出了一种基于深度学习的端到端方法。具体而言，我们将楼梯线检测过程视为涉及粗粒语义分割和对象检测的多任务。输入图像分为细胞，并使用简单的神经网络来判断每个单元是否包含楼梯线。对于包含楼梯线的细胞，相对于每个细胞的楼梯线的位置被回归。我们数据集上的广泛实验表明，我们的方法可以在速度和准确性方面达到高性能。轻量级版本甚至可以通过相同的分辨率实现每秒300多个帧。我们的代码和数据集将很快在GitHub上找到。
Staircases are some of the most common building structures in urban environments. Stair detection is an important task for various applications, including the environmental perception of exoskeleton robots, humanoid robots, and rescue robots and the navigation of visually impaired people. Most existing stair detection algorithms have difficulty dealing with the diversity of stair structure materials, extreme light and serious occlusion. Inspired by human perception, we propose an end-to-end method based on deep learning. Specifically, we treat the process of stair line detection as a multitask involving coarse-grained semantic segmentation and object detection. The input images are divided into cells, and a simple neural network is used to judge whether each cell contains stair lines. For cells containing stair lines, the locations of the stair lines relative to each cell are regressed. Extensive experiments on our dataset show that our method can achieve high performance in terms of both speed and accuracy. A lightweight version can even achieve 300+ frames per second with the same resolution. Our code and dataset will be soon available at GitHub.
