Purpose

Periodic inspection and maintenance are essential for effective pavement preservation. Cracks not only affect the appearance of the road and reduce the levelness, but also shorten the life of road. However, traditional road crack detection methods based on manual investigations and image processing are costly, inefficiency and unreliable. The research aims to replace the traditional road crack detection method and further improve the detection effect.

Design/methodology/approach

In this paper, a crack detection method based on matrix network fusing corner-based detection and segmentation network is proposed to effectively identify cracks. The method combines ResNet 152 with matrix network as the backbone network to achieve feature reuse of the crack. The crack region is identified by corners, and segmentation network is constructed to extract the crack. Finally, parameters such as the length and width of the cracks were calculated from the geometric characteristics of the cracks and the relative errors with the actual values were 4.23 and 6.98% respectively.

Findings

To improve the accuracy of crack detection, the model was optimized with the Adam algorithm and mixed with two publicly available datasets for model training and testing and compared with various methods. The results show that the detection performance of our method is better than many excellent algorithms, and the anti-interference ability is strong.

Originality/value

This paper proposed a new type of road crack detection method. The detection effect is better than a variety of detection algorithms and has strong anti-interference ability, which can completely replace traditional crack detection methods and meet engineering needs.

中文翻译：

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基于矩阵结构分割网络的道路裂缝检测与量化

目的

定期检查和维护对于有效的路面保护至关重要。裂缝不仅影响路面的美观，降低平整度，还会缩短路面的使用寿命。然而，传统的基于人工调查和图像处理的道路裂缝检测方法成本高、效率低且不可靠。该研究旨在替代传统的道路裂缝检测方法，进一步提高检测效果。

设计/方法/方法

本文提出了一种基于矩阵网络融合基于角点检测和分割网络的裂纹检测方法，以有效识别裂纹。该方法将ResNet 152与矩阵网络结合为骨干网络，实现裂缝的特征重用。通过角点识别裂缝区域，构建分割网络提取裂缝。最后，根据裂缝的几何特征计算出裂缝的长度和宽度等参数，与实际值的相对误差分别为4.23%和6.98%。

发现

为了提高裂纹检测的准确性，该模型使用 Adam 算法进行了优化，并混合了两个公开可用的数据集进行模型训练和测试，并与各种方法进行了比较。结果表明，我们方法的检测性能优于许多优秀的算法，抗干扰能力强。

原创性/价值

本文提出了一种新型的道路裂缝检测方法。检测效果优于多种检测算法，抗干扰能力强，可完全替代传统裂纹检测方法，满足工程需要。