CrackSegDiff：基于扩散概率模型的多模态裂缝分割
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack Segmentation
AI总结：
本文提出基于扩散概率模型的多模态裂缝分割方法CrackSegDiff，通过融合灰度与深度图像，结合Vm-unet提取全局特征并引入CFM和SFCM模块优化特征整合，在FIND数据集上优于现有方法，显著提升浅层裂缝检测性能。
摘要：
在道路巡检机器人中集成灰度与深度数据，可提升道路状况评估的准确性、可靠性与全面性，从而改善维护策略并提高基础设施安全性。然而，这些数据源常受到路面大量背景噪声的干扰。扩散概率模型（DPM）的最新进展在图像分割任务中展现出卓越成效，具备强大的去噪能力，如SegDiff研究所示。尽管如此，当前基于DPM的分割器仍未充分利用原始图像数据的潜力。本文提出一种用于裂缝分割的新型DPM方法，命名为CrackSegDiff，其独特地融合了灰度图像与距离/深度图像。该方法通过加强DPM局部特征提取与全局特征提取之间的交互来增强逆扩散过程。与使用Transformer处理全局特征的传统方法不同，本方法采用Vm-unet高效捕获原始数据的长程信息。通过两个创新模块：通道融合模块（CFM）和浅层特征补偿模块（SFCM），进一步优化了特征整合。在FIND数据集的三类裂缝图像分割任务上的实验评估表明，CrackSegDiff优于现有最先进方法，在浅层裂缝检测中表现尤为突出。代码可在https://github.com/sky-visionX/CrackSegDiff获取。
英文摘要：
Integrating grayscale and depth data in road inspection robots could enhance the accuracy, reliability, and comprehensiveness of road condition assessments, leading to improved maintenance strategies and safer infrastructure. However, these data sources are often compromised by significant background noise from the pavement. Recent advancements in Diffusion Probabilistic Models (DPM) have demonstrated remarkable success in image segmentation tasks, showcasing potent denoising capabilities, as evidenced in studies like SegDiff. Despite these advancements, current DPM-based segmentors do not fully capitalize on the potential of original image data. In this paper, we propose a novel DPM-based approach for crack segmentation, named CrackSegDiff, which uniquely fuses grayscale and range/depth images. This method enhances the reverse diffusion process by intensifying the interaction between local feature extraction via DPM and global feature extraction. Unlike traditional methods that utilize Transformers for global features, our approach employs Vm-unet to efficiently capture long-range information of the original data. The integration of features is further refined through two innovative modules: the Channel Fusion Module (CFM) and the Shallow Feature Compensation Module (SFCM). Our experimental evaluation on the three-class crack image segmentation tasks within the FIND dataset demonstrates that CrackSegDiff outperforms state-of-the-art methods, particularly excelling in the detection of shallow cracks. Code is available at https://github.com/sky-visionX/CrackSegDiff.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
