Tang Wenliang, Wu Ziyi, Wang Wei, Pan Youqin, Gan Weihua
School of Information and Software Engineering, East China Jiaotong University, Nanchang, 330013, China.
School of Transportation and Logistics, East China Jiaotong University, Nanchang, 330013, China.
Sci Rep. 2025 Mar 15;15(1):8938. doi: 10.1038/s41598-025-92994-7.
Cracks are common defects in physical structures, and if not detected and addressed in a timely manner, they can pose a severe threat to the overall safety of the structure. In recent years, with advancements in deep learning, particularly the widespread use of Convolutional Neural Networks (CNNs) and Transformers, significant breakthroughs have been made in the field of crack detection. However, CNNs still face limitations in capturing global information due to their local receptive fields when processing images. On the other hand, while Transformers are powerful in handling long-range dependencies, their high computational cost remains a significant challenge. To effectively address these issues, this paper proposes an innovative modification to the VM-UNet model. This modified model strategically integrates the strengths of the Mamba architecture and UNet to significantly improve the accuracy of crack segmentation. In this study, we optimized the original VM-UNet architecture to better meet the practical needs of crack segmentation tasks. Through comparative experiments on the Crack500 and Ozgenel public datasets, the results clearly demonstrate that the improved VM-UNet achieves significant advancements in segmentation accuracy. Compared to the original VM-UNet and other state-of-the-art models, VM-UNet++ shows a 3% improvement in mDS and a 4.6-6.2% increase in mIoU. These results fully validate the effectiveness of our improvement strategy. Additionally, VM-UNet++ demonstrates lower parameter count and floating-point operations, while maintaining a relatively satisfactory inference speed. These improvements make VM-UNet++ advantageous for practical applications.
裂缝是物理结构中常见的缺陷，如果不及时检测和处理，可能会对结构的整体安全构成严重威胁。近年来，随着深度学习的发展，特别是卷积神经网络（CNN）和Transformer的广泛应用，裂缝检测领域取得了重大突破。然而，CNN在处理图像时，由于其局部感受野，在捕捉全局信息方面仍面临局限性。另一方面，虽然Transformer在处理长距离依赖关系方面很强大，但其高计算成本仍然是一个重大挑战。为了有效解决这些问题，本文提出了一种对VM-UNet模型的创新性改进。这种改进后的模型战略性地整合了Mamba架构和UNet的优势，以显著提高裂缝分割的准确性。在本研究中，我们对原始的VM-UNet架构进行了优化，以更好地满足裂缝分割任务的实际需求。通过在Crack500和Ozgenel公共数据集上的对比实验，结果清楚地表明，改进后的VM-UNet在分割精度上取得了显著进步。与原始的VM-UNet和其他先进模型相比，VM-UNet++的平均Dice相似系数（mDS）提高了3%，平均交并比（mIoU）提高了4.6 - 6.2%。这些结果充分验证了我们改进策略的有效性。此外，VM-UNet++在保持相对令人满意的推理速度的同时，参数数量和浮点运算次数更少。这些改进使VM-UNet++在实际应用中具有优势。
