A Deep Learning-Based Assistive System for the Visually Impaired Using YOLO-V7
Bibliographic record
Abstract
Individuals with visual impairments frequently confront substantial difficulties in interacting with their environment, a problem that is often exacerbated by the cost and accessibility of existing assistive technologies.This study introduces a prototype for a costeffective and accessible assistive device that employs deep learning techniques for object recognition.The proposed system utilizes the YOLO-V7 model, a deep learning algorithm trained on a comprehensive dataset encompassing various everyday objects, including US dollar denominations.In conjunction with two transfer learning-based cascade models, the system offers detection across 86 object categories.Upon object identification, the name of the item is converted into a Braille-readable format using the Python Braille library.Comprehensive experiments and analyses were undertaken to assess the efficacy of the proposed system.The results corroborate the system's effectiveness in achieving its intended purpose, demonstrating its potential to significantly aid visually impaired individuals in recognizing and interacting with objects in their environment.With a processing and Braille code generation time of 188.5 ms per frame, the model achieved recall, precision, and mAP scores of 0.81, 0.92, and 0.96, respectively.The integration of deep learning technology with high-performance platform boards has facilitated the development of a promising solution to the challenges faced by visually impaired individuals in environmental interaction.Overall, the proposed prototype represents an accessible and cost-effective assistive device, potentially revolutionizing the manner in which visually impaired individuals interact with their surroundings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".