Multispectral Fusion with Adaptive Band Selection and Multilevel Hierarchical Decomposition for Robust Crack Detection in Generalized Environments

The quality and feature representation of crack images are critical for crack detection. Traditional RGB-based methods are intuitive but highly susceptible to environmental interferences. Multispectral imaging enhances detection robustness through spectral stability and band complementarity. Although fusing RGB with infrared or other multispectral bands has been applied to crack detection, existing methods often select fixed bands, lacking an adaptive band selection strategy that generalizes across environments. Additionally, most fusion approaches focus on mitigating environmental interferences under specific crack conditions, leading to poor model transferability and difficulty handling compound interferences such as illumination, temperature, occlusions, and complex backgrounds. Moreover, the superposition of environmental interferences poses greater challenges for feature extraction, boundary enhancement, image stabilization, and resolution improvement in sensitive area fusion. This paper proposes a Multispectral Fusion Approach based on Adaptive Band Selection and Multi-level Decomposition (MFAM). First, a multi-criteria adaptive band selection algorithm is proposed to extract the most discriminative spectral bands using environment-aware weighting. Second, the Dual-Scale Transform-Domain Dual-Nested Decomposition (DSTD-DND) strategy mitigates feature loss and fusion instability caused by poor environmental transferability, establishing hierarchical representation spaces via multi-level frequency band decomposition to precisely integrate multi-scale information. Finally, to mitigate boundary blurring and small-scale feature loss due to low spatial resolution, a spectral-guided enhancement re-fusion method is proposed. By re-fusing the preliminary fused image with the optimal band, edge details are refined, and spatial resolution and clarity enhanced. Experiments on some multispectral crack datasets demonstrate the superior environmental adaptability and image detail enhancement of the proposed MFAM.