由于桥梁结构会受到应变、温湿度等侵蚀和损毁，经常会造成桥梁结构产生裂缝病害，需要对桥梁结构的裂缝病害进行检测.设计了全卷积神经网络模型，采用裂缝图像数据集对模型进行了训练和验证，利用该全卷积神经网络模型可对3种桥梁裂缝病害的形态进行识别，并且与其他裂缝识别方法相比，具有较高的准确率和召回率.

With the rapid development of China's economy， the mileage scale of roads and railways is becoming larger and larger. As an important node of the road network， bridges have also achieved very rapid development. At the same time， because the bridge structure will be eroded and damaged by strain， temperature and humidity， it often causes crack diseases in the bridge structure. Therefore， based on the above reasons， it is necessary to detect the crack disease of the bridge structure in order to reasonably arrange personnel to maintain and maintain the bridge. In this paper， the full convolution neural network model is designed， and the crack image data set is used to train and verify the model， so as to achieve the purpose of crack recognition， and has high accuracy and recall. The full convolution neural network model can be used to identify the forms of three bridge crack diseases， and has great detection advantages compared with other crack identification methods. The theoretical analysis of this paper and the identification and detection technology for bridge crack diseases have positive guiding significance and application value for relevant research.