Semi-Supervised Semantic Segmentation Using Adversarial Learning for Pavement Crack Detection

Abstract

Regular inspection of pavement conditions is important to guarantee the safety of transportation. However, current approaches are time-consuming and subjective, which requires the technician to annotate each training image exactly pixel by pixel. To ease the workload of the inspector and lower the cost of acquiring the high-quality training dataset, a semi-supervised method for the pavement crack detection is proposed. Firstly, unlabeled pavement images can be used for the model training in our proposed algorithm, our model can generate a supervisory signal for unlabeled pavement images, which makes up for the deficiency of image annotation. Secondly, an adversarial learning method and a full convolution discriminator are adopted, which can learn to distinguish the ground truth from segmentation predictions. To improve the accuracy of pavement crack detection, the adversarial loss is coupled with the cross-entropy loss in discriminator. Thus, the quality of the training model is no longer dependent on the quantity of the labeled dataset and the accuracy of the labeled. Compared with existing methods that can only employ labeled images, our method utilizes unlabeled images to improve the pavement crack detection accuracy. Moreover, our model is validated on the CFD dataset and AigleRN dataset, the experimental results show that the proposed algorithm is effective. Compared with existing methods, not only can our method detect different types of cracks, but also be particularly effective when only a few labeled are available: when using 118 crack images with a resolution of 480 × 320, using only 50% of the labeled data, the detection accuracy of our model can reach 95.91%.