Adaptive Expert Selection for Crack Segmentation Using a Top-K Mixture-of-Experts Framework with Out-of-Fold Supervision

Cracks in civil infrastructure exhibit large variations in appearance due to differences in surface texture, illumination, and background clutter, making reliable segmentation a challenging task. To address this issue, this paper proposes an adaptive Mixture-of-Experts (MoE) framework that combines multiple crack segmentation models based on their estimated reliability for each input image. A lightweight gating network is trained using out-of-fold soft supervision to learn how to rank and select the most suitable experts under varying conditions. During inference, only the top two experts are combined to produce the final segmentation result. The proposed framework is evaluated on two public datasets—Crack500 and the CrackForest Dataset (CFD)—and one in-house dataset (RCFD). Experimental results demonstrate consistent improvements over individual models and recent state-of-the-art methods, achieving up to 2.4% higher IoU and 2.1% higher F1-score compared to the strongest single expert. These results show that adaptive expert selection provides an effective and practical solution for robust crack segmentation across diverse real-world scenarios.