# An Embedded Real-time Object Alert System for Visually Impaired: A Monocular Depth Estimation based Approach through Computer Vision

arXiv: https://arxiv.org/abs/2507.08165 · HTML: https://mlsanity.com/paper/2507.08165/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [Learning monocular depth estimation infusing traditional stereo knowledge](https://mlsanity.com/paper/1904.04144/) (2019)
- [RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving](https://mlsanity.com/paper/2401.07322/) (2024)
- [Investigating YOLO Models Towards Outdoor Obstacle Detection For Visually Impaired People](https://mlsanity.com/paper/2312.07571/) (2023)

## Built on by (most cited first, 1 of 1)

- [VisionAId: An Offline-First Multimodal Android Assistant for People with Visual Impairment, Featuring Personalized Object Retrieval](https://mlsanity.com/paper/2607.02371/) (2026)

## Abstract (arXiv)

Visually impaired people face significant challenges in their day-to-day commutes in the urban cities of Bangladesh due to the vast number of obstructions on every path. With many injuries taking place through road accidents on a daily basis, it is paramount for a system to be developed that can alert the visually impaired of objects at close distance beforehand. To overcome this issue, a novel alert system is proposed in this research to assist the visually impaired in commuting through these busy streets without colliding with any objects. The proposed system can alert the individual to objects that are present at a close distance. It utilizes transfer learning to train models for depth estimation and object detection, and combines both models to introduce a novel system. The models are optimized through the utilization of quantization techniques to make them lightweight and efficient, allowing them to be easily deployed on embedded systems. The proposed solution achieved a lightweight real-time depth estimation and object detection model with an mAP50 of 0.801.