基于深度学习的野外露头区岩石裂缝识别

针对当前地质考察中的野外露头区岩石裂缝及周围环境较为复杂且数据依赖人工描绘和传统的图像处理算法，其识别效率及准确度低下造成地质考察研究困难这一实际情况，提出一种基于深度学习的露头区岩石裂缝识别算法，从而提高岩石裂缝识别的准确度及效率。该方法基于TensorFlow架构，先将预处理的训练数据集图片进行人工挑选预处理为裂缝和背景2类图片，再将已分类的图片传入已设计完成的卷积神经网络模型进行训练并保存训练模型的参数数据，用已训练的模型数据对预处理的岩石裂缝图片进行识别并记录裂缝位置信息，通过裂缝位置信息对未预处理过的原色岩石裂缝图片进行裂缝定位并显示。实验结果表明所用方法可较高准确度地识别裂缝，为地质考察提供更准确便捷的裂缝识别方法。

Crack Recognition of Outcrop Area Based on Deep Learning

Aiming at the fact that the rock fractures and the surrounding environment in the field of outcrops in the current geological survey are more complicated and the data relies on manual depiction and traditional image processing algorithms, the recognition efficiency and accuracy are low, which makes the research of geological survey difficult, a deep learning rock fracture identification algorithm in outcrop areas is presented, thereby improving the accuracy and efficiency of rock fracture identification. This method is based on the TensorFlow architecture. First, the preprocessed training dataset pictures are manually selected and preprocessed into two types of pictures: cracks and backgrounds. Then the classified pictures are passed to the designed convolutional neural network model for training and saving the parameter data of the model, the trained model data is used to identify the preprocessed rock fracture pictures and record the fracture location information, and the fracture location information is used to locate and display the fractures of the unprocessed primary color rock fracture pictures. The experimental results show that the method can identify fractures with higher accuracy, and provide a more accurate and convenient fracture identification method for geological surveys.