Road Pothole Detection and Location System Based on YOLOv5 and Beidou GPS
Bibliographic record
Abstract
Road potholes are harmful to safe transportation, which will cause vehicle damage, poor ride comfort and put passengers in danger. Road pothole detection and result application is one of the key measures to solve the above problems. Therefore, this paper designs a road pothole detection and location system. The system mainly consists of edge computing platform (including detection algorithm), road pothole image acquisition module, positioning module, display module and auxiliary module. The computing platform adopts Jetson Nano. The road pothole detection algorithm adopts YOLOv5 algorithm. The positioning module adopts Beidou GPS module. First, the camera collects the image set of road potholes (or adopts an open image set). The image set is divided into two parts: training set and test set, which are used for training and testing respectively. Then, based on YOLOv5 algorithm, the road pothole images in the training set are trained, and the optimal target detection model is obtained. Finally, the model is used to test the road pothole images in the test set. The open road pothole image set is tested, and the road pothole recognition rate is above 90%. Through this system, road potholes can be accurately detected and the location information of potholes can be recorded. The research results in this paper can be provided to traffic management departments and used in unmanned vehicles, which is of great significance to reduce the impact of road potholes on safe driving of 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".