混凝土外观与流动态图像机器视觉识别综述

doi:10.19678/j.issn.1000-3428.0252172

摘要/Abstract

摘要：

在土木工程领域, 借助机器视觉技术对采集的混凝土产品图像进行识别, 可快速、准确且无损地评估混凝土性能, 对于工程应用具有重大意义。当前, 传统人工检测方法效率低、主观性强, 而现有图像识别技术面临光照不均、背景噪声干扰、裂缝形态多样、动态图像分界模糊等挑战, 亟需构建适应复杂工程场景的智能化解决方案。通过系统梳理有关文献, 聚焦两种状态混凝土评估, 即静态硬化混凝土裂缝与外观缺陷识别、动态新拌混凝土流动性能评估。首先, 从传统数字图像技术角度和神经网络角度分别综述现有研究在不同场景和拍摄主体下对于裂缝识别、外观质量判别、流动性评估的研究进展; 然后, 总结对比现有处理流程中预处理、图像分割、特征提取等步骤中不同算法的优劣与应用场景; 最后, 通过对比分析提出一套推荐的混凝土产品外观质量和流动性判断的图像识别处理流程和解决方案, 为结构混凝土性能的智能识别与评估提供算法思路, 以促进视觉技术在土木工程领域的应用。

关键词: 机器视觉, 混凝土产品, 裂缝识别, 外观质量, 流动性

Abstract:

Machine vision technology can be leveraged to identify images of collected concrete products, enabling the rapid, accurate, and nondestructive assessment of their performance, which is significant for engineering applications. Traditional manual inspection methods are inefficient and highly subjective. Moreover, the performance of existing image recognition technologies is hindered by challenges such as uneven lighting, background noise interference, diverse crack shapes, and blurry boundaries in dynamic images. Therefore, intelligent solutions that can adapt to complex engineering scenarios are in demand. Through a systematic review of relevant literature, this paper evaluates two concrete types, focusing on the identification of cracks and appearance defects in static hardened concrete and the evaluation of the flowability of dynamic fresh concrete. First, from the perspectives of traditional digital image technology and neural networks, it reviews the research progress on crack identification, appearance quality discrimination, and flowability assessment under different scenarios and shooting subjects. Then, it summarizes and compares the advantages and disadvantages of different algorithms in the preprocessing, image segmentation, and feature extraction steps in existing processing procedures, as well as their application scenarios. Finally, through a comparative analysis, a set of recommended image recognition processing procedures and solutions for judging the appearance, quality, and flowability of concrete products is proposed. This paper provides algorithmic ideas for the intelligent recognition and assessment of structural concrete performance, thereby promoting the application of visual technology in the civil engineering field.

Key words: machine vision, concrete products, crack identification, appearance quality, fluidity

徐晟轩, 许蕾, 费一凡. 混凝土外观与流动态图像机器视觉识别综述[J]. 计算机工程, 2026, 52(5): 43-59.

XU Shengxuan, XU Lei, FEI Yifan. A Review of Image Recognition Technology for Concrete Appearance and Flow State Based on Machine Vision[J]. Computer Engineering, 2026, 52(5): 43-59.

https://www.ecice06.com/CN/Y2026/V52/I5/43

图/表 16

图1 真空浸渍荧光显微图片

Fig.1 Vacuum impregnation fluorescence micrographs

图2 光照不均匀系数筛选前后图片

Fig.2 Pictures before and after screening based on light inhomogeneity coefficients

图3 基于骨架提取和分形理论的识别

Fig.3 Recognition based on skeleton extraction and fractal theory

图4 结合小波变换与水平投影法的接缝干扰去除

Fig.4 Seam interference removal combining wavelet transform and horizontal projection method

图5 水下图像增强

Fig.5 Underwater image enhancement

图6 图像处理流程图

Fig.6 Image processing flowchart

图7 TransUNet模型识别效果

Fig.7 Recognition effect of TransUNet model

图8 利用SNN排除相似效果

Fig.8 Effect of utilizing SNN for exclusion of similarities

图9 DeepLabv3+网络识别效果

Fig.9 Recognition effect of DeepLabv3+ network

图10 YOLOR-BDIM识别效果

Fig.10 Recognition effect of YOLOR-BDIM

图11 亮度梯度测量净浆流动度效果

Fig.11 Effect of brightness gradient measurement of slurry flowability

图12 钢板做参照的复杂背景下混凝土扩展度识别

Fig.12 Recognition of concrete slump with steel plate as a reference in complex background

图13 硬化混凝土图像识别推荐流程

Fig.13 Recommended usable process for hardened concrete image recognition

图14 利用视频流识别新拌混凝土流动性的结果

Fig.14 Result of using video streams to identify the fluidity of freshly mixed concrete

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