Evaluation of Pavement Surface Distress Using Image Processing and Artificial Neural Network

Pavement evaluation helps assess the structural and functional condition of roads. Traditional pavement evaluation methods cannot efficiently and accurately assess the state of road conditions at the network level. Though the pavement evaluation techniques based on high resolution cameras and laser sensors assess the road conditions efficiently at traffic speeds, there are a few limitations in respect of automotive detection and quantification of pavement surface distress. Though artificial intelligence is a broad area, machine learning applications in highway engineering were proven to be successful. This paper presents the automated pavement distress classification using a convolutional neural network (NN) from the Keras library. VGG-16, the deep convolutional NN (DCNN) model, was deployed by necessary modification to get the desired output. The model is trained on a big data set of images with a wide range of pavement defects and irregularities. A DCNN classifier trained with an "Adam" optimizer was used to change the features of the NN to minimize the loss. As an output, the model classifies the pavement surface distresses as alligator cracks, longitudinal cracks, transverse cracks, pothole, and no crack portion of the pavement with maximum accuracy using a DCNN classifier.