Road Pothole Detection and Location System Based on YOLOv5 and Beidou GPS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it