# Review of Recent Automated Pothole-Detection Methods
## 3 The Three Types of Recent Automated Pothole-Detection Methods
### 3D Reconstruction-Based Method (Type C)  
[PERSON] et al. [33] proposed four pothole-detection methods using computer vision and learning. Two methods are based on stereo-vision analysis of road environments ahead of the vehicle: the single-frame stereo-vision-based method and the multiframe fusion-based method. The other two methods are designed by using deep-learning techniques such as transfer learning with Mask R-CNN and transfer learning with YOLOv2. The authors experimented with the proposed four methods using the existing datasets [11; 17; 34; 35; 36] on websites and evaluated their performance in the aspects of precision, recall, and IoU. The experimental result showed that each method has its own benefits and can provide different pathways to a number of applications. The authors explained that the multiframe fusion-based method was suitable for identifying potholes and road manifolds with high accuracy. They also explained that transfer learning with YOLOv2 was capable of real-time pothole identification. [PERSON] et al. [37] proposed an automated pothole-detection method based on stereo imaging on roads using a hybrid-matching scheme that combines keypoint-matching and block-matching techniques. The proposed method consists of six phases: data acquisition, data preprocessing, stereo matching, 3D data generation, and pothole metrology. Static and dynamic images were collected from left and right cameras of the moving vehicle in the data-acquisition process. The dynamic images were filtered as a data preprocessing task before performing stereo matching. The hybrid-matching scheme was applied in the stereo-matching process using static and dynamic images. 3D data were generated by applying the stereo-triangulation process to the result of stereo matching. The 3D data was postprocessed for identifying the pothole and estimating the volume calculation. The performance of the proposed method was evaluated by accuracy qualification, stereo reconstruction, volume calculation, etc. The experimental result showed that the 3D measurements are within 3 mm for the static situation and 5 mm for the dynamic situation. It also showed that potholes are imaged to accuracies of \(-20\%\) for volume, \(-15\%\) for area, and \(-4\%\) for depth at 10 km/h vehicle speed when compared to static imagery. [PERSON] et al. [38] proposed an automated pixel-level pavement-distress-detection method based on stereo vision and deep learning. The proposed method consists of five phases: data acquisition, 3D reconstruction, 3D data processing, pixel-level pavement detection, pothole segmentation, and volume measurement. Parallel and oblique photos to be used as input data were obtained using the vehicle-mounted photography system. The 3D pavement point-cloud model was generated by applying stereo-vision technology to the input images in the 3D reconstruction phase. Point-cloud calibration was based on a principal-component-analysis (PCA) algorithm, and orthoimages such as color image, depth image, and overlapped image were generated in the 3D data-processing phase. A deep-learning technology such as a modified U-net was applied to segment a pavement crack and a pothole in orthoimages. Furthermore, the volume of a pothole was measured by integrating pothole segmentation and the proposed volume-measurement algorithm. The performance of the proposed method was evaluated by precision, recall, and F1-score. The experimental results showed that the precision, recall, and F1-score of the proposed method were 0.9632, 0.9552, and 0.9592. The authors explained that the performance of their proposed method is better than the performance of other existing methods [39; 40] in the aspects of pothole segmentation through the performance comparison. The experimental results also showed that the overall relative error was 4.62% when comparing the manual volume measurements and automated volume measurements. The proposed method also has high accuracy in measuring the volume of a pothole through the experimental results.