Real-time edge AI algorithm for road defects detection
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
Vision-based automated road defects detection schemes are significant for highway maintenance and road condition grade assessment. This paper was dedicated to achieving a refined trade-off between accuracy and efficiency through neural network optimisation. Specifically, we proposed a novel lightweight model, RT-RDD (Real-Time Road Defects Detection), which integrated low-level extraction and high-level semantic feature extraction for road defects detection. This was achieved by designing a more advantageous backbone structure and a lighter neck structure. Additionally, key improvements include optimising the feature extraction strategy and evaluation. The RT-RDD model surpasses existing models in terms of mean Average Precision (mAP), with only a marginal reduction after quantisation. Compared to foundation model, our algorithm improves mAP (0.5:0.95) by 4.2%. In addition, we propose a quantisation and compression strategy that effectively reduces the overall model size by approximately one-third. Notably, this reduction in size only results in a minor decrease of 1.3% in mAP (0.5:0.95). Testing on NVIDIA Jetson Xavier NX Development Board Kit demonstrates the model achieves a detection speed of 58 frames per second (FPS) with enhanced precision. These improvements make it ideal for deployment in practical road maintenance scenarios.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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 it