轻量级模型在模态缺失下实现高精度裂缝分割，兼顾鲁棒性与效率。
Compass: Degradation-Simulated Reciprocal Learning with Lightweight Needle RWKV for Multimodal Crack Segmentation under Missing Modalities
- 通过模拟严重缺失条件并双向蒸馏，分离完整感知与退化适应。
- 90%深度模态缺失时仍达F1 0.8216、mIoU 0.8434，仅需258万参数。
- 适合工业缺陷检测场景，尤其适用于多模态数据不全的部署环境。
在工业设施多模态裂缝分割中，核心挑战是防止模态缺失导致像素级性能下降，同时保持低计算成本。现有方法难以应对模态缺失引发的语义退化问题。本文提出Compass，一种针对任意模态缺失的轻量级鲁棒裂缝分割网络。Compass包含退化模拟蒸馏（DSD）、针状模块（Needle Block）和证据拓扑保持融合（ETPF）。DSD构建退化模拟流，模拟更严重的缺失情形，并与原始流进行互蒸馏，解耦完整感知与退化适应。其中，特征感知原型传输器（FAPT）实现模态无关的原型引导特征补全，保障不完整条件下的语义一致性。作为轻量骨干网络，Needle将裂缝方向线索注入WKV调制，结合连通性感知门控与各向异性上下文探测，实现结构感知建模。ETPF通过Dempster-Shafer证据组合与不确定性门控解码融合多模态特征，保留裂缝拓扑结构的同时抑制不可靠特征。在三个数据集上的实验表明，Compass在多种缺失场景下达到当前最优（SOTA）性能。即使在CrackDepth数据集上深度模态缺失90%，仍可实现F1 0.8216、mIoU 0.8434，参数量仅2.58M。代码已开源。
原文摘要 · Abstract (English)
In multimodal crack segmentation for industrial facilities, the key challenge is preventing missing modalities from degrading pixel-level performance while maintaining low computational cost. Existing methods struggle to address semantic degradation caused by missing modalities. We propose Compass, a lightweight network for robust crack segmentation under arbitrary missing modalities. Compass comprises Degradation Simulation Distillation (DSD), Needle Block, and Evidential Topology-Preserving Fusion (ETPF). DSD constructs a degradation simulation stream that mimics more severe missing conditions and performs reciprocal distillation with the original stream, decoupling complete perception from degradation adaptation. Within DSD, Feature-Aware Prototype Transmitter (FAPT) performs modality agnostic prototype-guided feature completion to maintain semantic integrity under incomplete modality conditions. As a lightweight backbone, Needle injects crack-direction cues into WKV modulation and combines connectivity-aware gating with anisotropic context probing for structure-aware modeling. ETPF fuses multimodal features via Dempster-Shafer evidential combination with uncertainty-gated decoding, preserving crack topology while suppressing unreliable features. Experiments on three datasets demonstrate state-of-the-art (SOTA) performance under diverse missing modality scenarios. Even with 90\% depth modality missing on CrackDepth, Compass achieves F1 of 0.8216 and mIoU of 0.8434 with only 2.58M parameters. The code is available at https://github.com/Karl1109/Compass.
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