Computer Vision-based Assistance System for the Visually Impaired Using Mobile Edge Artificial Intelligence
Bibliographic record
Abstract
Despite significant recent developments, visual assistance systems are still severely constrained by sensor capabilities, form factor, battery power consumption, computational resources and the use of traditional computer vision algorithms. Current visual assistance systems cannot adequately perform complex computer vision tasks that entail deep learning. We present the design and implementation of a novel visual assistance system that employs deep learning and point cloud processing to perform advanced perception tasks on a cost-effective, low-power mobile computing platform. The proposed system design circumvents the need for expensive, power-intensive Graphical Processing Unit (GPU)-based hardware required by most deep learning algorithms for real-time inference by employing instead edge Artificial Intelligence (AI) accelerators such as the Neural Compute Stick-2 (NCS2), model optimization techniques such as OpenVINO, and TensorFlow Lite, and smart depth sensors such as OpenCV AI Kit-Depth (OAK-D). Critical system design challenges such as training data collection, real-time capability, computational efficiency, power consumption, portability and reliability are addressed. The proposed system includes more advanced functionality than existing systems such as assessment of traffic conditions and detection and localization of hanging obstacles, crosswalks, moving obstacles and sudden elevation changes. The proposed system design incorporates an AI-based voice interface that allows for user-friendly interaction and control and is shown to realize a simple, cost-effective, power-efficient, portable and unobtrusive visual assistance device.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".