Evaluating Real-Time Object Detection Models for Autonomous Vehicle Vision
Bibliographic record
Abstract
Object detection has made remarkable advancement in recent years particularly through CNN-based algorithms, essential for autonomous vehicles. These vehicles need efficient detection systems that balance speed and accuracy in complex environments. The study evaluates several real-time object detection models, such as YOLOv10, YOLOv11, EfficientDet, Faster R-CNN, and SSD focusing on both single-stage and two-stage detectors. Using the Berkeley DeepDrive 100K image dataset and iRain dataset, the study assesses the performance of these models in identifying key road-related objects. Additionally, the paper introduces an enhanced object detection system designed to improve robustness and accuracy in challenging conditions. The results emphasize the crucial role these advanced models have in improving the environmental awareness of autonomous vehicles, leading to better safety and operational performance on the road. Experimental data demonstrate that the proposed system surpasses current models, providing a scalable and dependable solution to boost the safety and efficiency of autonomous systems in real-world settings.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| 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 | 0.002 | 0.001 |
| 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".