基于图像的混凝土表面裂缝量化高效识别方法

卷积神经网络（convolutional neural network，CNN）算法是目前进行裂缝图像识别的常用方法。但目前仍存在卷积神经网络过于复杂、训练参数多、设备配置要求高和检测实时性低等问题。针对以上问题，本文提出一种基于轻量化CNN的混凝土表面裂缝识别方法。通过搭建轻量化全卷积神经网络（light-weight full convolutional neural network，LFNet）解决目前经典的卷积神经网络中训练参数过多的问题；采用基于高斯梯度变化的阈值分权法，对存在裂缝的图像进行分析，提取裂缝特征；最后采用基于欧氏距离的裂缝宽度算法实现对裂缝宽度分析计算。实验结果表明，本文所提的LFNet优于目前经典的卷积神经网络，其精确率、召回率和综合评价函数值三个参数分别达到97.944%、98.277%、98.108%，裂缝宽度特征参数的计算误差可控制在0.5 mm以内。

Image-based quantitative and efficient identification method for concrete surface cracks

The convolutional neural network(CNN)algorithm is commonly used in automated crack detection,but its current version is too complicated involving many training parameters,high equipment configuration requirements,and low detection real-time performance.This paper develops a lightweight CNN method(LFNet).This simplified version of CNN reduces the number of training parameters,and then analyzes and extracts cracking features from the images of cracked concrete through a threshold division weight method based on Gaussian gradient change.Finally,it calculates the crack width using a Euclidean distance algorithm.Comparison with experimental results shows LFNet is better than previous methods of classical convolutional neural network and achieves an accuracy,recall and F1 value of 97.9%,98.3%and 98.1%respectively.Its calculation errors of characteristic crack widths can be controlled within a range of 0.5 mm.