Distribution-aware Noisy-label Crack Segmentation
This addresses noisy label issues in crack segmentation for robotic infrastructure inspection, but it is incremental as it builds on the SAM-Adapter with a novel joint learning approach.
The paper tackles performance degradation in crack segmentation due to noisy labels in small datasets by introducing a joint learning framework that uses distribution-aware semantic knowledge to guide a SAM-Adapter, resulting in significant outperformance over state-of-the-art methods and high cross-domain generalization on unseen datasets.
Road crack segmentation is critical for robotic systems tasked with the inspection, maintenance, and monitoring of road infrastructures. Existing deep learning-based methods for crack segmentation are typically trained on specific datasets, which can lead to significant performance degradation when applied to unseen real-world scenarios. To address this, we introduce the SAM-Adapter, which incorporates the general knowledge of the Segment Anything Model (SAM) into crack segmentation, demonstrating enhanced performance and generalization capabilities. However, the effectiveness of the SAM-Adapter is constrained by noisy labels within small-scale training sets, including omissions and mislabeling of cracks. In this paper, we present an innovative joint learning framework that utilizes distribution-aware domain-specific semantic knowledge to guide the discriminative learning process of the SAM-Adapter. To our knowledge, this is the first approach that effectively minimizes the adverse effects of noisy labels on the supervised learning of the SAM-Adapter. Our experimental results on two public pavement crack segmentation datasets confirm that our method significantly outperforms existing state-of-the-art techniques. Furthermore, evaluations on the completely unseen CFD dataset demonstrate the high cross-domain generalization capability of our model, underscoring its potential for practical applications in crack segmentation.