A Real-Time Indoor Object Detection and Distance Estimation System for Visually Impaired Individuals
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
For individuals with visual impairments, navigating indoor environments can present significant challenges due to the absence of adequate signage. This is due in large part to the lack of adequate signage, which can hinder their ability to move safely and independently. Although installing proper signage can lower the risk of accidents, it does not fully resolve the issue and may require considerable costs and time. In response to this challenge, this paper presents our approach to providing real-time assistance for visually impaired individuals in indoor settings, utilizing an ESP32 microcontroller and advanced image recognition models like YOLOv8. The main goal is to offer an effective system for object detection and accurate distance estimation, thereby improving the mobility and safety of users. The system employs a camera to capture real-time images, which are sent to a server for processing and obstacle identification. The processed information is then relayed to the user through headphones, providing auditory cues that aid safe navigation. The results showcase high precision in object detection, with an accuracy of 85%, and distance estimation, reflected by a determination coefficient of 0.97578. Our system presents an enhanced solution to mobility challenges in indoor environments, promoting greater autonomy and social inclusion for visually impaired individuals.
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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 | 0.001 | 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 it