Comparison of Residual Network and Other Classical Models for Classification of Interlayer Distresses in Pavement

Abstract

Many automatic classification methods published can identify the main hidden distress types of highways, but they cannot meet the precise needs of operation and maintenance. The classification of interlayer distresses based on ground penetrating radar (GPR) images is very important to improve maintenance efficiency and reduce cost. However, among models of different complexities, which models are suitable for the interlayer distress data needs further verification. Firstly, to cover enough of the variable range of distress samples, the interlayer distress dataset collected containing 32,038 samples was subcategorized into three types: interlayer debonding, interlayer water seepage, and interlayer loosening. Secondly, residual networks (ResNets) that render easier to build shallower or deeper networks (ResNet-4, ResNet-6, ResNet-8, ResNet-10, ResNet-14, ResNet-18, ResNet-34, and ResNet-50) and five classical network models (DenseNet-121, EfficientNet B0, SqueezeNet1_0, MobileNet V2, and VGG-19) were evaluated by training and validation loss, test accuracy, and model complexity. The experimental results show that all models have high test accuracy with little difference, but ResNet-4, ResNet-6, SqueezeNet1_0, and ResNet-8 exhibit no overfitting which means they have good generalization performance.