Weakly supervised single-stage crack detection based on multi-scale feature fusion

Abstract

Cracks pose a significant threat to road and building safety, making effective detection of cracks on road surfaces a focus of research both domestically and internationally. Deep learning-based methods often require extensive pixel-level annotations, posing significant labor costs. We propose a single-stage weakly supervised crack segmentation model based on multi-scale feature fusion. The model is built on a single-stage weakly supervised segmentation framework, which reduces model complexity. It utilizes a multi-scale feature fusion module (PPM) to integrate features at different scales, enhancing the ability to extract features from cracks of various sizes. The combination of the Domain Restriction Suppression (DRS) module and pixel affinity convolution is employed to optimize pseudo-pixel annotations. In addition, we propose a joint loss function to mitigate sample imbalance between crack and non-crack pixels. Compared to other two-stage weakly supervised segmentation models, our model is simpler and more effective, achieving excellent results on the Deep Crack and Crack500 datasets, surpassing most weakly supervised crack segmentation models in terms of Recall (Re), F-score (F1), and mean Intersection-over-Union (mIoU), achieving similar effects to fully supervised crack segmentation models. This demonstrates the effectiveness and robustness of our model.