用扩散模型解决细长裂缝分割难题，性能领先现有方法8%。
CrossDiff: Diffusion Probabilistic Model With Cross-conditional Encoder-Decoder for Crack Segmentation
- 设计跨条件编码解码结构，增强细节与语义特征提取
- 在5个数据集上实现Dice和IoU均提升8.0%
- 适合工业混凝土裂缝检测场景，尤其擅长细长裂缝
工业混凝土表面的裂缝分割因裂缝形态复杂、纤细难辨而极具挑战。传统分割方法难以准确定位此类裂缝，影响维护效率。本文提出首个用于裂缝分割的扩散概率模型——CrossDiff，其采用跨条件编码解码结构，构成十字形扩散架构。跨编码器强化裂缝细节保留能力，跨解码器提升裂缝语义特征提取效果，从而更优处理细长裂缝。在包含CFD、CrackTree200、DeepCrack、GAPs384和Rissbilder在内的五个挑战性数据集上进行大量实验，结果表明，CrossDiff在Dice分数和交并比（IoU）上均优于现有最先进方法，提升达8.0%。代码即将开源。
原文摘要 · Abstract (English)
Crack Segmentation in industrial concrete surfaces is a challenging task because cracks usually exhibit intricate morphology with slender appearances. Traditional segmentation methods often struggle to accurately locate such cracks, leading to inefficiencies in maintenance and repair processes. In this paper, we propose a novel diffusion-based model with a cross-conditional encoder-decoder, named CrossDiff, which is the first to introduce the diffusion probabilistic model for the crack segmentation task. Specifically, CrossDiff integrates a cross-encoder and a cross-decoder into the diffusion model to constitute a cross-shaped diffusion model structure. The cross-encoder enhances the ability to retain crack details and the cross-decoder helps extract the semantic features of cracks. As a result, CrossDiff can better handle slender cracks. Extensive experiments were conducted on five challenging crack datasets including CFD, CrackTree200, DeepCrack, GAPs384, and Rissbilder. The results demonstrate that the proposed CrossDiff model achieves impressive performance, outperforming other state-of-the-art methods by 8.0% in terms of both Dice score and IoU. The code will be open-source soon.
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