# Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera
## 3 Results  
In this section, the accuracies of various deep neural network architectures are presented to determine the best one. Only the hyperparameters of the convolution layer were varied, since it is the most computationally intensive layer. After conducting several experiments with different filters, the best architecture was identified, and only some of the results are presented. Figure 9 depicts the training and validation progress, as well as the accuracy of the proposed deep neural network (Model 1) for detecting speed bumps and potholes in our database. In this case, 36 filters of dimensions \(5\times 5\) were used. Figure 8: Convolutional neural network architecture with their hyperparameters and parameters. Figure 10 shows the progress of training and validation, as well as the accuracy of the proposed deep neural network called Model 2. It used 37 filters of \(5\times 5\) pixels, with an accuracy of 92.06%. Figure 11 shows the progress of training and validation, as well as the accuracy of the proposed deep neural network called Model 3. It used 38 filters of \(5\times 5\) pixels, with an accuracy of 64.95%. Figure 10: Model 2: Neural network accuracy using 37 filters of \(5\times 5\) pixels, accuracy 92.06%. Figure 9: Model 1: Neural network accuracy using 36 filters of \(5\times 5\) pixels, accuracy 91.12%. Figure 12 shows the progress of training and validation, as well as the accuracy of the proposed deep neural network called Model 4, which used 37 filters of 7 \(\times\) 7 pixels, with an accuracy of 57.94%. Figure 11: Model 3: Neural network accuracy using 38 filters of 5 \(\times\) 5 pixels, accuracy 64.95%. Figure 12: Model 4: Neural network accuracy using 37 filters of 7 \(\times\) 7 pixels, accuracy 57.94%. Figure 14 shows the progress of training and validation, as well as the accuracy of the proposed deep neural network called Model 6, which used 38 filters of 3 \(\times\) 3 pixels with an accuracy of 83.18%. _Feature Visualization of Convolutional Neural Network_  
Convolutional neural networks use filters that extract complicated features from a window of 3 \(\times\) 3, 5 \(\times\) 5, to \(p\times p\), where \(p\) is usually odd. The network learns these  
Figure 14: Model 6: Neural network accuracy using 38 filters of 3 \(\times\) 3 pixels, accuracy 83.18%. Figure 13: Model 5: Neural network accuracy using 37 filters of 3 \(\times\) 3 pixels, accuracy 98.13%. Pothole and bump detection systems based on images provide us with the opportunity to predict the presence of these anomalies on the road in order to avoid them. In contrast, accelerometers do not allow this because they require a very sudden movement to detect a pothole or speed bump. On the other hand, when we compared our proposal with similar works, we identified several articles that detected potholes and speed bumps using accelerometers, lidar, and images. However, the closest works to ours were those proposed by [PERSON] et al., and by [PERSON] et al. [9; 22]. The first proposed a system on a smartphone running Inception V2 to detect potholes in Czech, Indian, and Japanese cities. Although they reported that their dataset was composed of 9053 road damage images captured with a smartphone, unfortunately, that dataset only contained images of potholes photographed from inside the car, not from the angle of a camera on a car to detect or predict the existence of anomalies on the road. The second proposed an image classification with potholes that were specifically photographed, which does not allow for capturing the conditions in which a driver would observe a pothole in the distance from a car. However, it is also a lightweight architecture that has eight layers of image processing. In contrast, our database includes three types of images: those with speed bumps, with potholes, and without either. Moreover, the images were taken from the front of a car. Our deep neural network uses only 7 layers, which is lighter for the processing hardware compared to Inception V2 with 12 layers.