Robust Lane Line Detection for Intelligent Vehicles under Complex Illumination Conditions Based on Image Processing
Bibliographic record
Abstract
With the rapid advancement of intelligent driving technologies, visual navigation has become a core approach for environmental perception in autonomous vehicles.The accuracy of lane line detection under complex illumination directly limits the reliability of autonomous navigation.Existing methods face significant challenges: traditional threshold segmentation requires uniform lighting conditions; edge detection algorithms often produce false edges under strong light; deep learning methods suffer from high computational complexity and degraded feature extraction in low-light scenarios; and image enhancement techniques like histogram equalization (HE) struggle to adapt to dynamic lighting changes.To address these issues, this study proposes an integrated solution combining image enhancement and lane line detection.On one hand, an optimized Multi-Scale Retinex (MSR) method is employed to improve illumination component estimation and reflectance recovery, enhancing contrast in lane line images under complex lighting.On the other hand, a Sparrow Search Algorithm (SSA) is introduced to optimize the similarity matrix construction and cluster center initialization in spectral clustering, enabling precise separation of lane lines from the background.The proposed approach offers a robust and real-time solution for reliable navigation of intelligent vehicles in unstructured lighting environments, contributing significantly to the visual perception theory and advancing the industrialization of autonomous driving.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".