PaveSAM – segment anything for pavement distress

Abstract

Automated pavement monitoring using computer vision can analyse pavement conditions more efficiently and accurately than manual methods. Accurate segmentation is essential for quantifying the severity and extent of pavement defects and consequently, the overall condition index used for prioritising maintenance activities. Deep-learning-based segmentation models are however, often supervised and require pixel-level annotations, which can be costly and time-consuming. While the recent evolution of zero-shot segmentation models can generate pixel-wise labels for unseen classes without any training data, they struggle with irregularities of cracks and textured pavement backgrounds. This research proposes a zero-shot segmentation model, PaveSAM, which can segment pavement distresses using bounding box prompts. By retraining SAM's mask decoder with just 180 images, pavement distress segmentation is revolutionised, enabling efficient distress segmentation using boundingbox prompts, a capability not found in current segmentation models. This drastically reduces labelling costs and establishing the pioneering use of SAM in pavement distress segmentation.