A Novel SGD-U-Network-Based Pixel-Level Road Crack Segmentation and Classification

Abstract

Abstract Automatic road crack detection plays a major role in developing an intelligent transportation system. The traditional approach of in-situ inspection is expensive and requires more man-power. In-order to solve this problem, a novel approach for automatic road crack segmentation was developed using Stack Generative adversarial network Discriminator-U-Network (SGD-U-Network). We have collected 19 300 crack and non-crack images (MIT-CHN-ORR dataset) from the Outer Ring Road of Chennai, TamilNadu, India. The MIT-CHN-ORR dataset was initially pre-processed using traditional image processing techniques for ground truth image generation. A stage-I and stage-II stack Generative Adversarial Network (GAN) model was introduced for generating high-resolution non-crack images. Then, the extracted features from Stack GAN Discriminator of stage II (SGD2) was concatenated with every level of expansion path in SGD-U-Network for segmenting the crack regions of the input crack images. Also, multi-feature-based classifier was developed using the features extracted from SGD2 and the bottleneck layer of SGD-U-Network. Our proposed model was implemented on MIT-CHN-ORR dataset and also analyzed our model performance using other existing benchmark datasets. The experimental analysis showcased that the proposed method outperformed the other state-of-the-art approaches.