基于卷积神经网络的桥梁裂缝检测与识别  

Detection and recognition of bridge crack based on convolutional neural network

作　　者：刘洪公 王学军[1] 李冰莹[1] 孟洁[1] 

机构地区：[1]石家庄铁道大学信息科学与技术学院,河北石家庄050043

出　　处：《河北科技大学学报》2016年第5期485-490,共6页Journal of Hebei University of Science and Technology

基　　金：河北省教育厅重点项目(ZD2016052)

摘　　要：针对当前中国检测桥梁裂缝依赖人工目测,危险系数极大的落后现状,研究了一种基于数字化和智能化的检测方法,以提高桥梁安全诊断效率,降低危险系数。结合机器视觉和卷积神经网络技术,利用Raspberry Pi处理器采集和预处理图像,分析裂缝图像的特点,选取效果检测和识别裂缝效果最佳处理算法,改进裂缝分类的卷积神经网络模型(CNN),最终提出一种新的智能裂缝检测方案。实验结果显示:该方案能够找到超出桥梁裂缝最大限值的所有裂缝,并可以有效识别裂缝类型,识别率达90%以上,能够为桥梁裂缝检测提供参考数据。Aiming at the backward artificial visual detection status of bridge crack in China,which has a great danger coefficient,a digital and intelligent detection method of improving the diagnostic efficiency and reducing the risk coefficient is studied.Combing with machine vision and convolutional neural network technology,Raspberry Pi is used to acquire and pre-process image,and the crack image is analyzed;the processing algorithm which has the best effect in detecting and recognizing is selected;the convolutional neural network（CNN）for crack classification is optimized;finally,a new intelligent crack detection method is put forward.The experimental result shows that the system can find all cracks beyond the maximum limit,and effectively identify the type of fracture,and the recognition rate is above 90%.The study provides reference data for engineering detection.

关 键 词：图像处理 桥梁裂缝 RASPBERRY PI 卷积神经网络 检测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]