An optimized deep belief network based pothole detection model for asphalt road

The poor quality of asphalt roads has a significant impact on driver safety, damages the mechanical structure of vehicles, increases fuel consumption, annoys passengers and is sometimes also responsible for accidents. Further, the poor quality of the road can be described as a rough surface and the presence of potholes. The potholes can be one of the main reasons for accident cause, increased fuel consumption and annoying passengers. Furthermore, the potholes can be of varied size, radiance effect, shadow and scales. Hence, the detection of potholes in asphalt roads can be considered a complex task and one of the serious issues regarding the maintenance of asphalt roads. This work focuses on the detection of the potholes in the asphalt roads. So in this work, a pothole detection model is proposed for accurate detection of potholes in the asphalt roads. The effectiveness of the proposed pothole detection model is tested over a set of real-world image datasets. In this study, the asphalt roads of the Delhi-NCR region are chosen and real-world images of these roads are collected through the smart camera. The final road image dataset consists of a total of 1150 images including 860 pothole images and the rest of are without pothole images. Further, the deep belief network is integrated into a proposed model for the detection of pothole images as a classification task and classified the images as pothole detected and not pothole. The experimental results of the proposed detection model are evaluated using accuracy, precision, recall, F1-Score and AUC parameters. These results are also compared with ANN, SVM, VGG16, VGG19 and InceptionV3 techniques. The simulation results showed that the proposed detection model achieves a 93.04% accuracy rate, 94.30% recall rate, 96.31% precision rate and 96.92% F1-Score rate than other techniques.