Benchmarking YOLOv4–YOLOv11 for Autonomous Driving: Small-Object Detection, Adverse Conditions and Confidence Calibration
Bibliographic record
Abstract
Autonomous vehicles rely on real‑time object detection to perceive their surroundings and make safety‑critical decisions. The You Only Look Once (YOLO) family of one‑stage detectors is attractive for embedded platforms because it delivers high throughput; however, achieving high accuracy, fast inference and reliable confidence estimation simultaneously remains challenging. This study investigates how detection‑head design (anchor‑based vs. anchor‑free), intersection‑over‑union (IoU) loss functions and post‑processing strategies (standard non‑maximal suppression (NMS) vs. NMS‑free training) influence both accuracy and calibration for autonomous‑driving scenarios. Experiments were conducted on the BDD100K validation split using a unified training recipe with 640×640 images, consistent data augmentations and identical hyper‑parameters across eight configurations. Mean Average Precision (mAP), Expected Calibration Error (ECE), Brier score and end‑to‑end inference speed (frames per second, FPS) were measured alongside an error taxonomy for small objects. To further improve confidence reliability, a simple post‑hoc temperature‑scaling calibration was applied and evaluated. The results show that an anchor‑free head with a Complete‑IoU (CIoU) loss and NMS‑free training achieves the best accuracy–efficiency trade‑off, reducing ECE from 2.6 % to 2.1 % and increasing throughput to 97 FPS without sacrificing mAP. Temperature scaling further decreases ECE by approximately 0.5 percentage points and improves low‑confidence precision–recall area. These findings demonstrate that carefully chosen architectural and post‑processing design choices can significantly improve both the accuracy and trustworthiness of YOLO‑based detectors for autonomous vehicles.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".