# Small Object Detection for Indoor Assistance to the Blind using YOLO NAS Small and Super Gradients

arXiv: https://arxiv.org/abs/2409.07469 · HTML: https://mlsanity.com/paper/2409.07469/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 28 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://mlsanity.com/paper/1906.09756/) (2019)
- [Make RepVGG Greater Again: A Quantization-aware Approach](https://mlsanity.com/paper/2212.01593/) (2022)
- [Deep Learning based Wearable Assistive System for Visually Impaired People](https://mlsanity.com/paper/1908.03364/) (2019)
- [Real-Time Accident Detection in Traffic Surveillance Using Deep Learning](https://mlsanity.com/paper/2208.06461/) (2022)
- [YOLOBench: Benchmarking Efficient Object Detectors on Embedded Systems](https://mlsanity.com/paper/2307.13901/) (2023)
- [Motion Informed Object Detection of Small Insects in Time-lapse Camera Recordings](https://mlsanity.com/paper/2212.00423/) (2022)
- [Fast Training Data Acquisition for Object Detection and Segmentation using Black Screen Luminance Keying](https://mlsanity.com/paper/2405.07653/) (2024)

## Abstract (arXiv)

Advancements in object detection algorithms have opened new avenues for assistive technologies that cater to the needs of visually impaired individuals. This paper presents a novel approach for indoor assistance to the blind by addressing the challenge of small object detection. We propose a technique YOLO NAS Small architecture, a lightweight and efficient object detection model, optimized using the Super Gradients training framework. This combination enables real-time detection of small objects crucial for assisting the blind in navigating indoor environments, such as furniture, appliances, and household items. Proposed method emphasizes low latency and high accuracy, enabling timely and informative voice-based guidance to enhance the user's spatial awareness and interaction with their surroundings. The paper details the implementation, experimental results, and discusses the system's effectiveness in providing a practical solution for indoor assistance to the visually impaired.