Research on Pothole Detection and Avoidance Unmanned Vehicle System Based on YOLOv8 and Raspberry Pi
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
In order to reduce the harm of road potholes to the safe driving of unmanned vehicles, it is necessary to create an efficient and accurate road pothole detection and avoidance strategy. Therefore, this paper proposes a road pothole detection and avoidance unmanned vehicle (PDA-UV) system based on YOLOv8 and Raspberry Pi. The system mainly includes unmanned vehicle, road pothole detection, avoidance motion controller and image sensor. YOLOv8 is used as a road pothole detection algorithm. The motion controller of unmanned vehicle takes Raspberry Pi 4B/4G as the core and four Mecanum wheels as the motion mechanism of unmanned vehicle. Firstly, the system obtains the road pothole image through the camera; Then, the road pothole detection model is obtained after training with YOLOv8 algorithm, and the collected road scenes are tested. Finally, the road pothole detection model is deployed to Raspberry Pi 4B/4G, and the real-time motion control of the unmanned vehicle is carried out according to the identified road pothole results, so as to realize the avoidance function of the unmanned vehicle to the road pothole. In this paper, the experimental results of road potholes detection and avoiding single road potholes are given. The experimental results show that the unmanned vehicle can accurately detect road potholes and realize the avoidance motion control of a single road pothole according to the preset trajectory at low speed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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