Training a Neural Network for Lane Demarcation Detection in the Infrared Spectrum
Bibliographic record
Abstract
The retro-reflective characteristics of lane demarcations on roadways can potentially provide robust detection in the infrared spectrum even in poor lighting and weather conditions. This paper explores this idea by training a convolutional neural network using Darknet with YOLO to detect 9 classes of road lines from the Berkeley Deep Drive Dataset (BDD). Although BDD is composed of conventional colour images, they were converted to greyscale prior to training as a solution to the scarcity of datasets in the infrared spectrum. The trained model was evaluated on road scenes acquired by the infrared sensor of an Intel-Realsense camera. From the experimental results, it is concluded that object detection techniques primarily developed for localization and classification of objects in the form of bounding boxes are inherently unsuitable for detecting line shaped objects such roadway lane demarcations. In addition, despite the sub-optimal training and detection approach, the performance showed potential for robust lane detection using infrared images.
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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 | 0.000 | 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 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".