| 摘 要: 针对深层卷积神经网络检测表面结构裂纹耗费时间长、精度不够高的问题，基于Xception网络进行自适应调整重构其分类器，利用图像增广技术扩充数据集后，引入迁移学习的方法对Xception网络进行训练。同时，与构建的ResNet50、InceptionV3和VGG19三个深层卷积神经网络模型进行对比实验，重新验证其性能。实验证明，引入迁移学习不仅可以提升模型的整体性能，还能缩减训练深层卷积神经网络的时间，训练的模型在数据集上的识别精确率达到96.24%，在对比实验中达到96.50%。 |
| 关键词: 迁移学习；卷积神经网络；图像识别；图像增广 |
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中图分类号: TP399
文献标识码: A
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| 基金项目: 贵州省科技计划资助项目(黔科合平台人才[2019]5802号)；基于人工智能的动态监测系统关键技术开发及应用研究(黔科合支撑[2019]2886号). |
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| Research on Concrete Crack Identification based on Transfer Learning and Xception |
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XU Zhengyun1, QIAN Songrong1,2
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( 1.College of Mechanical Engineering, Guizhou University, Guiyang 550025, China;
2.State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China )
xu.zhengyun@qq.com; 909718747@qq.com
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| Abstract: Aiming at the problems of time consuming and low accuracy of deep convolution neural network in detecting surface structural cracks, this paper proposes to adaptively adjust and reconstruct its classifier based on Xception, and use image augmentation technology to expand the dataset. After that, transfer learning method is introduced for training Xception. At the same time, its performance is re-verified by comparing it with three deep convolution neural network models, ResNet50, InceptionV3 and VGG19. Experiments show that the introduction of transfer learning not only improves the overall performance of the model, but also reduces the time of training the deep convolutional neural network. Recognition accuracy of the trained model on the dataset reaches 96.24%, and 96.50% in the comparative experiment. |
| Keywords: transfer learning; convolutional neural network; image recognition; image augmentation |
