Research on Small Target Detection Algorithm for Autonomous Vehicle Scenarios
Bibliographic record
Abstract
In recent years, road traffic object detection has gained prominence in areas such as traffic monitoring, autonomous driving, and road safety. Nonetheless, existing algorithms offer room for improvement, particularly when detecting distant or inherently small targets, such as vehicles and pedestrians, from camera perspectives. By addressing the detection accuracy issues associated with small targets, this study introduces the YOLOv5s‐LGC detection algorithm. This model incorporates a multiscale feature fusion network and leverages the lightweight GhostNet module to reduce model parameters. Furthermore, the GC attention module is employed to mitigate background interference, thereby enhancing the average detection accuracy across all categories. Through data analysis, target detection at different scales and sampling rates is determined. Experiments indicate that the YOLOv5s‐LGC model surpasses the baseline YOLOv5s in detection accuracy on the Partial_BDD100K and KITTI datasets by 3.3% and 1.6%, respectively. This improvement in locating and classifying small targets presents a novel approach for applying object detection algorithms in road traffic scenarios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".