面向混凝土裂缝检测的级联神经网络算法研究

针对传统深度学习的裂缝检测方法在复杂环境下鲁棒性低、边缘区域识别精度差、损伤量化结果误差大的问题，本文提出一种复杂环境下基于级联神经网络的混凝土裂缝检测方法。该方法分为三步：第一步利用改进的语义分割模型对复杂环度参数获取算法计算裂缝宽度。试验结果表明，相较于传统裂缝识别方法，本文方法在精确率、召回率、准确率、F1分数和交并比五项评价指标上均有提升，且总体检测准确率在95%以上，能实现复杂环境下境下的裂缝进行初步识别，判断图像中裂缝的大致感兴趣区域；第二步采用本文所提基于金字塔池化的掩膜优化方法对粗分割图像进行优化，精确捕获裂缝边缘上下文信息；第三步采用二维码靶标的图像像素解析度和裂缝宽混凝土裂缝的检测与定量分析。

Research on cascaded neural network algorithm for concrete crack detection

This paper presents a concrete crack detection method based on cascaded neural networks in complex environments, aiming at the problems of the traditional deep learning crack detection method in complex environments: low robustness, poor edge area identification accuracy, and large errors in damage quantification results. In this three-step method, first it uses the improved semantic segmentation model to preliminarily identify cracks in complex environments, and determines roughly a cracking area of interest in the image. Then, it optimizes the rough segmentation image using the mask based on pyramid pooling to accurately capture the context information of the crack edge. Finally, it calculates crack width using the image pixel resolution with the QR code targets and a crack width parameter acquisition algorithm. The test results show that compared with the traditional crack identification, this method improves significantly in the five evaluation indicators－precision rate, recall rate, accuracy rate, F1 score and intersection ratio－and achieves an overall detection accuracy of higher than 95%, thereby realizing the detection and quantitative analysis of concrete cracks in complex environments.