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陈健昌,张志华. 融于图像多特征的路面裂缝智能化识别[J]. 科学技术与工程, 2021, 21(24): 10491-10497.
Chen Jianchang,Zhang Zhihua.Intelligent recognition of pavement cracks based on multiple image features[J].Science Technology and Engineering,2021,21(24):10491-10497.
融于图像多特征的路面裂缝智能化识别
Intelligent recognition of pavement cracks based on multiple image features
投稿时间:2021-01-17  修订日期:2021-06-04
DOI:
中文关键词:  路面裂缝  裂缝检测  深度学习  卷积神经网络  图像分类  图像识别
英文关键词:pavement crack  crack detection  deep learning  convolutional neural network  image classification  image recognition
基金项目:国家重点研发计划(2017YFB0504201, 2017YFB0504203), 国家自然科学基金(41861059,41761082, 61862039), 兰州交通大学优秀平台支持(201806)
     
作者单位
陈健昌 兰州交通大学测绘与地理信息学院
张志华 兰州交通大学测绘与地理信息学院
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中文摘要:
      路面裂缝形状不规则复杂程度高。传统路面裂缝识别技术需要对路面图像进行复杂预处理工作进行识别,不能自动化对路面裂缝图像进行分类。为提高对路面裂缝识别精度和效率,本文将基于深度学习方法提出一种自动识别路面裂缝并能减少图像预处理工作量的方法。首先,将原始图像切割为小样本图像,根据图像多特征进行分类,各选取相同类型样本2000张图像构建数据集;其次,利用双线性内插法对裁剪后图像进行上采样,凸显图像特征便于神经网络学习;最后,使用深度学习神经网络对训练样本进行特征提取训练模型。实验结果表明:ResNet101模型评估指标均优于其他深度学习模型和机器学习模型,模型测试精度达0.898,kappa系数为0.815。
英文摘要:
      The pavement crack shape is irregular and complex. When the pavement crack image is recognized by the traditional pavement crack identification technology, it needs complex pre-processing work. The image of pavement crack cannot be automatically recognized by traditional pavement crack technology. In order to improve the accuracy and efficiency of identifying pavement cracks, a method for automatically identifying road cracks and reducing the workload of image preprocessing was proposed based on deep learning. Firstly, the original image was cut into small sample images, which were classified according to the multiple features of the image. Each data set was composed of 2000 images with same type sample; Secondly, the cropped image was up-sampled using bilinear interpolation. The image features were highlighted to facilitate network learning; Finally, the features of the training samples were extracted by the deep learning neural network and the training model was generated. The experimental results show that the evaluation indicators of the ResNet101 model are better than other deep learning models and machine learning models. The ResNet101 model is tested with accuracy of 0.898, and the kappa coefficient of ResNet101 is 0.815.
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