SMARTGUIDE: Revolutionizing the Depth and Dependability of Vision-Impaired Navigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Globally, over 2.2 billion people face vision impairment, necessitating innovative solutions for safe, independent navigation. Traditional aids like canes, guide dogs, and GPS offer basic support but lack the sophistication to provide contextual understanding, precise navigation, or real-time hazard alerts. This project presents SmartGuide, a mobile app designed to enhance the independence of visually impaired users through AI-driven features. SmartGuide offers three main functions: (1) Smart Vision, using the GPT-4 Vision API to deliver spoken feedback about surroundings; (2) Navigation, combining QR code detection via YOLO with ZoeDepth for depth estimation, guiding users to destinations through the shortest path calculated by Dijkstra's algorithm; and (3) Obstacle Detection and Alerts, where YOLO identifies obstacles, and ZoeDepth estimates their distance to inform users of potential hazards. By adapting its responses based on user feedback, SmartGuide provides personalized, reliable guidance that empowers visually impaired individuals to navigate with confidence and safety, advancing the field of accessible technology.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it