Integrated APC-GAN and AttuNet Framework for Automated Pavement Crack Pixel-Level Segmentation: A New Solution to Small Training Datasets

Abstract

Pavement crack segmentation using deep learning methods can improve crack segmentation accuracy, but in many cases the training dataset is lacking or uneven, making it insufficient to train an accurate segmentation model. In this work, an integrated APC-GAN and AttuNet framework is proposed as an automated pavement surface crack pixel-level segmentation solution for small training datasets. First, an Automated Pavement Crack Generative Adversarial Network (APC-GAN) is designed for the pavement cracks data as an image augmentation method, which is modified and improved from a traditional Deep Convolutional Generative Adversarial Network (DCGAN). Then, a novel pixel-level semantic segmentation structure, Attention modified U-Net (AttuNet), is proposed by introducing the attention module into the convolutional network structure. In order to assess the performance of our proposed framework, an open-source dataset DeepCrack is used, which only contains 300 training images. The results show that our proposed APC-GAN could augment the datasets by producing more clear and distinct pavement images than DCGAN and the generated images could feature more diversity than traditional image augmentation methods. APC-GAN demonstrated higher accuracy than DCGAN and traditional image augmentation methods. Apparently, our proposed APC-GAN and AttuNet framework gains the highest value in the evaluation metrices, including recall, F1 score, mean Intersection over Union (mIoU) and mean Pixel Accuracy (mPA) among all models including U-Net, DeepLabv3, FCN, and LRASPP.