Research on the design and realization of interactive wearable blindness guidance system based on computer vision
Bibliographic record
Abstract
Aiming at the problems of single function of navigation aids and limited applicable scenes for visually impaired people traveling at present, this paper proposes an interactive wearable guide system based on computer vision technology. The system integrates a variety of sensors, including infrared rangefinder, ultrasonic sensors and high-definition camera, and is equipped with STM32 microcontroller for efficient data processing, realizing real-time perception of the surrounding environment and accurate identification of obstacles. The system adopts YOLOV7 algorithm to intelligently analyze road conditions and provide accurate and real-time navigation information for visually impaired users through voice and vibration feedback. The system is also equipped with gyroscope and microphone for monitoring the user's movement status and receiving voice commands to realize natural human-computer interaction. The accompanying smartphone APP connects to the system wirelessly via Bluetooth and provides voice and vibration alerts through the headset and built-in motor, providing a convenient user interface. The APP integrates GPS positioning and Baidu map service, which not only records the user's walking route in real time, but also intelligently plans the traveling route and provides voice navigation service. The system is well-designed, integrating advanced technology and humanized interaction, aiming to provide an innovative, reliable and easy-to-use guide solution for the visually impaired.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".