## 4 Results and Discussion  
### Experiment Description  
#### 4.1.1 Dataset  
Eight datasets are used in this paper, including two datasets that were not augmented and the datasets that were processed with negative, fogging, and negative samples mixed with fog enhancement for two datasets.  
To solve the real problem of pothole detection, we create a dataset containing pictures of potholes with shadows and water accumulation similar to the road background. In addition, the shape and size of the potholes in the dataset are different, as shown in Figure 1. The self-made and augmented datasets are shown in Table 1. We also visualized the types of pictures contained in the self-made and augmented datasets in Figure 8.  
1. The self-made dataset, A1 in Table 1, includes 994 pictures of potholes, 795 pictures in the training set, 199 pictures in the test set, and the ratio of the training set to the test set is 8:2.
2. The negative sample dataset, A2 in Table 1, includes 746 pictures of potholes under normal conditions in the baseline dataset A1 and 248 pictures of manholes. The training set includes 795 pictures, and the test set contains 199 pictures. The ratio of the training set to the test set is 8:2.
3. The fogging dataset, referred to as A3 in Table 1, includes 746 pictures of potholes under normal conditions in the baseline dataset A1 and 248 pictures of potholes after the fogging treatment. The training set includes 795 pictures, and the test set contains 199 pictures. The ratio of the training set to the test set is 8:2.
4. The fogging and negative sample dataset, A4 in Table 1, includes 498 pictures of potholes in normal conditions in the baseline dataset A1, 248 pictures of potholes in the fogging dataset, and 248 pictures of manholes in the negative sample dataset. Thetraining set includes 795 pictures, and the test set contains 199 pictures. The ratio of the training set to the test set is 8:2.  
The Pothole600 dataset [50] was originally used for image segmentation, and we process it for labeling for the object detection. The Pothole600 dataset has smaller potholes, which can be used to verify the accuracy of our method for detecting smaller potholes. The details of the pothole600 dataset are shown in Table 2. We also visualized the types of pictures contained in pothole600 and the augmented datasets in Figure 9.  
1. The pothole600 dataset, B1 in Table 2, contains pictures of 600 potholes. The training set includes 480 pictures, and the test set contains 120 pictures. The ratio of the training set to the test set is 8:2.
2. The negative pothole600 sample dataset, B2 in Table 2, includes 450 original pothole600 pothole pictures and 150 manhole pictures. The training set includes 480 pictures, and the test set contains 120 pictures. The ratio of the training set to the test set is 8:2.
3. The pothole600 fogging dataset, B3 in Table 2, includes 450 original pothole600 pothole pictures and 150 fogging-treated pictures. The training set includes 480 pictures, and the test set contains 120 pictures. The ratio of the training set to the test set is 8:2.
4. The pothole600 fogging and negative sample dataset, B4 in Table 2, includes 300 pictures of potholes under normal conditions in the baseline dataset B1, 150 pictures of potholes treated with fogging in the fogging dataset B3, and 150 pictures of manholes in the negative sample dataset B2. The training set consists of 450 pictures, the test set contains 150 pictures, and the ratio of the training set to the test set is 8:2.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name** & **Pothole** & **Fog** & **Manhole** \\ \hline Pothole(A1) & 994 & 0 & 0 \\ Pothole-manhole(A2) & 746 & 0 & 248 \\ Pothole-fog(A3) & 746 & 248 & 0 \\ Pothole-manhole-fog(A4) & 498 & 248 & 248 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of photos of each type in A1, A2, A3, A4 of the self-made dataset.  
Figure 8: Self-made datasets and augmented number of pictures in the dataset.