Saliency-guided stairs detection on wearable RGB-D devices for visually with Swin-Transformer

Accuracy stairs detection is crucial for people with visual impairment, as it can reduce the potential unforeseen risks of falling on stairs. Wearable RGB-D technology can assist blind and visually impaired individuals. However, existing stair detection algorithms on RGB-D images face difficulties in the stair material, texture, lighting, and direction. In this study, we proposed a saliency-guided stairs detection method based on SwinTransformer to address the challenges mentioned above. First, saliency detection based on RGB-D images is used to learn spatial information for fast stair localization. Furthermore, we use the Swin-Transformer that incorporates key depth features of the stairs to solve orientation detection deficiencies. To evaluate the performance of our proposed method, we collected 3,290 RGB-D images, including the indoor and outdoor staircases. Experiments on our dataset show that our method can achieve high performance in terms of detection accuracy.