# Deep Learning based Wearable Assistive System for Visually Impaired People

arXiv: https://arxiv.org/abs/1908.03364 · HTML: https://mlsanity.com/paper/1908.03364/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2019.

## Built on by (most cited first, 12 of 12)

- [Trans4Trans: Efficient Transformer for Transparent Object Segmentation to Help Visually Impaired People Navigate in the Real World](https://mlsanity.com/paper/2107.03172/) (2021)
- [VIAssist: Adapting Multi-modal Large Language Models for Users with Visual Impairments](https://mlsanity.com/paper/2404.02508/) (2024)
- [Trans4Trans: Efficient Transformer for Transparent Object and Semantic Scene Segmentation in Real-World Navigation Assistance](https://mlsanity.com/paper/2108.09174/) (2021)
- [Open Scene Understanding: Grounded Situation Recognition Meets Segment Anything for Helping People with Visual Impairments](https://mlsanity.com/paper/2307.07757/) (2023)
- [HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor](https://mlsanity.com/paper/2107.03180/) (2021)
- [Generating Contextually-Relevant Navigation Instructions for Blind and Low Vision People](https://mlsanity.com/paper/2407.08219/) (2024)
- [Perception Framework through Real-Time Semantic Segmentation and Scene Recognition on a Wearable System for the Visually Impaired](https://mlsanity.com/paper/2103.04136/) (2021)
- [AI-Powered Assistive Technologies for Visual Impairment](https://mlsanity.com/paper/2503.15494/) (2025)
- [ViT Cane: Visual Assistant for the Visually Impaired](https://mlsanity.com/paper/2109.13857/) (2021)
- [Small Object Detection for Indoor Assistance to the Blind using YOLO NAS Small and Super Gradients](https://mlsanity.com/paper/2409.07469/) (2024)
- [SeeSay: An Assistive Device for the Visually Impaired Using Retrieval Augmented Generation](https://mlsanity.com/paper/2410.03771/) (2024)
- [EmoAssist: Emotional Assistant for Visual Impairment Community](https://mlsanity.com/paper/2502.09285/) (2025)

## Abstract (arXiv)

In this paper, we propose a deep learning based assistive system to improve the environment perception experience of visually impaired (VI). The system is composed of a wearable terminal equipped with an RGBD camera and an earphone, a powerful processor mainly for deep learning inferences and a smart phone for touch-based interaction. A data-driven learning approach is proposed to predict safe and reliable walkable instructions using RGBD data and the established semantic map. This map is also used to help VI understand their 3D surrounding objects and layout through well-designed touchscreen interactions. The quantitative and qualitative experimental results show that our learning based obstacle avoidance approach achieves excellent results in both indoor and outdoor datasets with low-lying obstacles. Meanwhile, user studies have also been carried out in various scenarios and showed the improvement of VI's environment perception experience with our system.