The occurrence of fatigue cracks is an inherent part of the design of engineering structures subjected to nonconstant loads. Thus, the accurate description of cracks in terms of location and evolution during service conditions is mandatory to fulfill safety‐relevant criteria. In the present work, we implement a deep convolutional neural network to detect crack paths together with their crack tips based on displacement fields obtained using digital image correlation. To this purpose, fatigue crack propagation experiments were performed for AA2024‐T3 rolled sheets using specimens with different geometries. Several hundred datasets were acquired by digital image correlation during the experiments. A part of the displacement data from one of the specimens was then used to train the neural network. The results show that the method can accurately detect the shape and evolution of the cracks in all specimens. Adding synthetic data generated by finite element analyses to the training step improved the accuracy for cracks with stress intensity factors that exceeded the range of the original training data.