# Review of Recent Automated Pothole-Detection Methods
## 3 The Three Types of Recent Automated Pothole-Detection Methods
### A Vision-Based Method (Type A)
The average of the classification accuracy and precision of the proposed method were 0.7786 and 0.8345. The accuracy and precision of pothole detection presented as experimental results of this study are restricted to one pothole in the image data. On the other hand, it is confirmed that the accuracy of pothole detection was relatively low when there were multiple potholes in one image datum. In addition, it is possible to detect the shape of a pothole by applying the proposed method, but there are limitations when estimating the actual size of a pothole. Authors presented a method of extracting detailed features to predict the actual size of a pothole as a future work. [PERSON] et al. [12] presented a method for automated pothole detection that applied different YOLO models using images as input data. Three YOLO models such as YOLOv4, YOLOv4-tiny, and YOLOv5 were applied in the process of training and testing. The dataset of [12], which is found in [13], was composed of 665 pothole images and was divided into training, validation, and testing subsets. First of all, the images in the training subset were converted to be suitable for the various YOLO models. The models were trained and validated until the loss function reached a steady-state line. Next, the performance of three YOLO models was evaluated using mean average precision at 50% intersection-over union threshold ([EMAIL_ADDRESS]). The experimental results showed that the mean average precision as 0.5 ([EMAIL_ADDRESS]) of YOLOv4, YOLOv4-tiny, and YOLOv5s were 0.777, 0.787, and 0.748, respectively. It showed that the performance of YOLOv4-tiny was better than the performance of YOLOv4 and YOLOv5s for pothole detection. The low accuracy when detecting small potholes located at a long distance is a limitation of this study. In addition, it is judged as a limitation that the study was not carried out in bad weather conditions and under insufficient light conditions. [PERSON] et al. [14] presented a method for automated pothole detection that applied prepooling CNN using images as input data. The 400 raw pothole images were collected from different pavements under different light conditions. All images were cropped into 96,000 small images and they were used as a training or testing dataset of the proposed method. The dataset built on the pothole images collected by the authors of [14] was not open to the public. Authors explained the characteristics of prepooling CNN compared to the conventional CNN. The prepooling CNN is characterized by inserting the prepooling layer as an input before the first convolution layer of the conventional CNN. The advantages of the proposed method were analyzed in three aspects. It was demonstrated that the prepooling layer can improve the precision of pothole detection by comparing the average precision of the prepooling CNN and the conventional CNN. It was confirmed that the outputs of the prepooling CNN were little affected by different light and different pavement material conditions through MSE. It was also demonstrated that the CNN-based methods are more suitable for pothole detection than other conventional methods such as Sobel edge detection and K-means clustering analysis. [PERSON] et al. [15] proposed a pothole-detection method based on location-aware convolutional neural networks using images as input data. The proposed method consists of two main subnetworks such as a localization network (LCNN) and a part-based classification network (PCNN). The localization subnetwork employs a high recall network to find as many candidate regions as possible, and the part-based subnetwork performs classification on the candidates on which the network is expected to focus. Authors used two different sizes of image as input the network to achieve accuracy and efficiency of the proposed method. The performance of the proposed method was evaluated by accuracy, precision, recall, and F1 score using the public pothole dataset [16; 17].