基于深度学习的图像分析处理技术在建筑外墙裂缝特征检测中的应用研究

A Study on the Application of Image Analysis Processing Technology Based on Deep Learning in the Detection of Features of Cracks in Building Exterior Walls

作为一种常见的建筑物病害,建筑裂缝不仅影响建筑物的美观和使用寿命,还可能危及建筑物的结构安全。因此,准确检测和计算建筑外墙裂缝的特征参数,对于保障建筑物的安全和延长其使用寿命具有重要的意义。通过应用基于深度学习的图像分析处理技术,结合深度学习模型的优势,提升对小尺寸裂缝检测能力和对非均匀背景的适应性,为建筑外墙裂缝检测提供更为先进和可靠的解决方案,同时为深度学习技术在建筑领域的应用提供了新思路。

As the protective and aesthetic layer of a building,the exterior wall of a building plays an important role.Due to long-term exposure to natural environments,temperature changes,earthquakes,and other factors,it is prone to cracking and other problems.Not only do these cracks affect the appearance and lifespan of a building,but they can also jeopardize its structural integrity.Consequently,accurately detecting and measuring the characteristic parameters of cracks in building exteriors is of paramount importance for ensuring the safety and sustainable development of structures.By leveraging image analysis techniques based on deep learning,combined with the strengths of deep learning models,we can enhance the capability to detect small-scale cracks and adapt to non-uniform backgrounds.This provides a more advanced and reliable solution for the detection of cracks in building exteriors,and also offers new insights into the application of deep learning technologies in the field of architecture.