Abstract: 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.

Key words: computer vision, TensorFlow, crack recognition of outcrop area, CNN, geological survey