Design and Implementation of Smart Guide Glasses for the Blind Based on Deep Perception and Bone Conduction Technology
Bibliographic record
Abstract
Visually impaired individuals face challenges in independent mobility, as traditional assistive devices have limitations in terms of single-dimensional environmental perception and delayed real-time interaction. Current mainstream guide devices rely on ultrasound or basic obstacle avoidance logic, whose technical architecture struggles to analyze the dynamic spatial depth of complex urban environments. Technological breakthroughs in miniaturized RGB-D cameras and ToF sensors have opened up new possibilities, while bone conduction technology provides an open auditory channel, laying the foundation for non-invasive navigation. This paper delves into the cross-modal collaboration mechanism between depth perception and bone conduction, achieving three major innovations in its technical framework: lightweight depth computing units perform millimeter-level scene modeling with the support of embedded visual processors; spatial sound field modeling technology drives bone conduction audio directional prompts; and edge computing architecture ensures the efficiency of multi-sensor spatiotemporal fusion. The system identifies the attributes of dynamic obstacles through semantic segmentation algorithms, utilizes infrared assistance to mitigate strong light interference, and establishes a redundant verification mechanism for rainy and foggy environments. The ultimate goal is to reconstruct the spatial cognitive paradigm of visually impaired individuals and provide centimeter-level environmental understanding capabilities.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".