Advanced Detection of Helmet Usage and Number Plates in Riders
Bibliographic record
Abstract
Most critically, enforcing road safety laws and reducing roadway fatalities from traffic accidents will rely heavily on accurately detecting helmet use and motorcycle riders' number plates. Conventional methods of enforcement rely heavily on human observation and labour-intensive methods, and are problematic as they can be subject to human error under specific conditions (for example, poor weather and lighting). To demonstrate an automated system that could potentially mitigate some of these constraints, we developed a novel automated system by combining cutting-edge object detection models (You Only Look Once, Nano version (YOLOv8n), Faster R-CNN) and EasyOCR (optical character recognition) to assist law enforcement. YOLOv8n has been praised for its speed and accuracy; when developing and training the model, we sourced a dataset with images of riders wearing helmets and other images of riders without helmets, as well as the number plate images to ensure that during training, the model was able to learn both tasks as it related to the helmet use detection and number plates. EasyOCR is an excellent resource for extracting textual data from detected number plates, so combining YOLOv8n with EasyOCR allows for real-time data collection from the detected number plates. This unified platform is a big step ahead in roadway security technology. It offers an easily expandable yet successful way to keep an eye on helmet use and identify and punish drivers who don't wear them, all of which will help reduce traffic accidents and save lives.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".