An Unsupervised Learning Approach for Pavement Distress Diagnosis via Siamese Networks

Abstract

Accurate, automated diagnosis of pavement distress is essential for effective roadway maintenance but presents considerable challenges. Supervised learning methods are constrained by limited labeled data, while existing unsupervised representation learning approaches are difficult to capture the fine-grained details needed for precise pixel-level segmentation in pavement images with similar backgrounds. To address these limitations, we propose a novel unsupervised approach for pavement distress segmentation that employs a new pretext task within Siamese networks. Our method integrates an explicit prediction head and a high-dimensional cross-entropy loss, enabling implicit class labeling and enhancing fine-grained recognition of distress patterns. Additionally, vision transformers are employed to leverage self-attention mechanisms, facilitating accurate segmentation of foreground distress regions. Experimental results demonstrate that our approach outperforms existing unsupervised representation learning and anomaly detection methods. Notably, when used to pre-train backbone networks such as ResNet-50, our method yields higher accuracy and faster convergence on downstream supervised tasks compared to pre-training on the labeled ImageNet dataset. The proposed method holds promise for advancing pavement maintenance decision-making and enhancing the performance of traditional supervised deep learning models.