Crack plays a critical role in the field of evaluating the quality of concrete structures, which affects the safety, applicability, and durability of the structure. Due to its excellent performance in image processing, the convolutional neural network is becoming the mainstream choice to replace manual crack detection. In this paper, we improve the EfficientNetB0 to realize the detection of concrete surface cracks using the transfer learning method. The model is designed by neural architecture search technology. The weights are pretrained on the ImageNet. Supervised learning uses Adam optimizer to update network parameters. In the testing process, crack images from different locations were used to further test the generalization capability of the model. By comparing the detection results with the MobileNetV2, DenseNet201, and InceptionV3 models, the results show that our model greatly reduces the number of parameters while achieving high accuracy (0.9911) and has good generalization capability. Our model is an efficient detection model, which provides a new option for crack detection in areas with limited computing resources.

中文翻译：

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基于转移学习的卷积神经网络混凝土裂缝检测

裂缝在评估混凝土结构质量方面起着至关重要的作用，这会影响结构的安全性，适用性和耐久性。由于卷积神经网络在图像处理方面的出色表现，它已成为取代手动裂缝检测的主流选择。在本文中，我们改进了EfficientNetB0以实现使用转移学习方法检测混凝土表面裂缝。该模型是通过神经体系结构搜索技术设计的。权重在ImageNet上进行了预训练。监督学习使用Adam优化器来更新网络参数。在测试过程中，使用了来自不同位置的裂纹图像来进一步测试模型的泛化能力。通过将检测结果与MobileNetV2，DenseNet201和InceptionV3模型进行比较，结果表明，我们的模型大大减少了参数数量，同时达到了高精度（0.9911），并且具有良好的泛化能力。我们的模型是一种高效的检测模型，它为计算资源有限的区域提供了一种新的裂缝检测选项。