# Road Pavement Damage Detection Based on Local Minimum of Grayscale and Feature Fusion
## 3 Results and Discussion  
The GAPs v2 dataset is the most extensive dataset in the pavement distress domain that provides standardized, high-quality images [30]. The data of the pavement damage, collected by a Pulnix TM2030 camera on a S.T.I.E.R. car, contain cracks, pot slots, open joints, applied patches and inlaid patches. The GAPs v2 dataset includes a total of 2468 gray-valued images (8 bit), partitioned into 1417 training images, 51 validation images, 500 validation-test images and 500 test images. The image resolution is 1920 \(\times\) 1080 pixels with a per pixel resolution of 1.2 \(\times\) 1.2 mm. To facilitate deep learning research, they also created a smaller subset named 50 k for fast experiments, whose size is 160\(\times\) 160 pixels. This 50 k dataset also contains four pavement datasets, which are the training set, validation set, validation-test set and test set. In this work, the image blocks extracted from the dataset are used to train the CNN model rather than the dataset itself. To avoid confusion, we renamed these four datasets to be test0, test1, test2 and test3, and we test our model on each dataset independently. Each dataset is separated into a train set and a test set at a ratio of 1 : 1. As stated above, our model is based on the image blocks rather than the whole image window. We construct four training datasets of image blocks, extracting image blocks of  
Figure 3: (**a**) The detected image blocks in the image window. (**b**) Most image blocks belong to the identical crack. 32 \(\times\) 32 pixels individually from the four training sets, with 160 \(\times\) 160 pixels in 50 k via the proposed feature-points method. These image blocks, marked with a serial number, are used to train the CNN model to extract the features. The image blocks of the damage field are labeled by a positive sample, and those of normal pavement are by a negative sample. After constructing the training datasets, we then train the CNN model, combine the features and test the model based on the 50 k dataset. The detailed algorithm flow chart of our experiment is shown in Figure 4.  
As shown, the feature points based on the local minimum points of grayscale are picked up in the image window. Then, the image blocks of these feature points are cut into a square of 32 \(\times\) 32 pixels and input into the trained CNN model. For each image block, the fully connected layer of our CNN model can output a 64-dimensional feature vector. These feature vectors are ordered by the grayscale of the corresponding feature points. The top \(m\) feature vectors are combined in series to form a composite \(m\times\) 64-dimensional feature vector. The composite feature vectors from the training set are input into the classifier constructed by an SVM [31], whose kernel is the Radial Basis Function (RBF), with the parameters \(C=0.9\), \(Tol=1\times 10^{-3}\) and Gamma of default value in the scikit-learn library. Finally, the images in the test set are input into the proposed detection system to test the accuracy. All the image processing, coding, compiling and training are conducted on a personal computer with Anaconda3 and OpenCV in a Windows10 operating system. Figure 5 shows the accuracy of our model for the four datasets, test0, test1, test2 and test3, as a function of the number of feature points \(m\). As expected, the accuracy increases as the feature points increase for all four datasets and tends to saturation when \(m>5\). The accuracy for all four datasets can reach beyond 0.883 for test3 and even 0.973 for test1 by selecting only 5\(\sim\)7 feature points. Although the model constructing, training dataset and test dataset of our model are very different from the existing CNN model, we could still perform a rough comparison. In a previous report, the authors employed the train dataset of 50 k to train the ResNet34 model and test the model on the other three subsets [30]. The F1  
Figure 4: The detailed algorithm flow chart of our experiment.