Convolutional neural network for pothole detection in asphalt pavement

Abstract

Many image processing techniques (IPTs) were proposed to inspect pavement defects for improving the precision and efficiency of the on-site inspections by humans. However, the various pavement conditions led to the unacceptable stability of IPTs. Therefore, an application of convolutional neural networks (CNNs) is presented for pothole detection using digital images in this study. Two CNNs, called conventional CNN and pre-pooling CNN, were designed, trained, and tested using 96,000 pavement images. Additionally, a stability study and comparative study were conducted based on the testing results. The main difference between the two CNNs was that a pre-pooling layer was used in the pre-pooling CNN to pre-process pavement images before the first convolutional layer. The results demonstrated that the optimised pre-pooling CNN had the 98.95% recognition precision in the testing. The stability study indicated that the optimised CNN model had the robustness in various real-world situations (e.g. light conditions and pavement materials). Compared with the traditional IPT methods, the CNN had a higher precision for extracting pothole features autonomously.