Research Article
Pothole Detection Using Deep Learning: A Real-Time and AI-on-the-Edge Perspective
Table 4
Comparison with state-of-the-art vs. our results.
| Contribution | Dataset | [email protected] | [email protected] | Inference time (ms) |
| Omar et al. [43] | Pothole image dataset (PID) | 60% | — | — | Shaghouri et al. [44] | Pothole image dataset (PID) | 75.53% | — | — | Gajjar et al. [45] | Self + Online collected | — | 18.5% | 481 | Sung-Sik et al. [46] | Pothole image dataset (PID) | — | 74.8% | — | Our trained SSD-Mobilnetv2 | Pothole image dataset (PID) | — | 47.4% | 7 | Our trained YOLOv3 | Pothole image dataset (PID) | 83.60% | — | 70.57 | Our trained YOLOv4 | Pothole image dataset (PID) | 85.48% | — | 52.51 | Our trained YOLOv5 | Pothole image dataset (PID) | — | 95% | 10 |
|
|