This work aims at developing a machine learning-based model to detect cracks on concrete surfaces. Such model is intended to increase the level of automation on concrete infrastructure inspection when combined to unmanned aerial vehicles (UAV). The developed crack detection model relies on a deep learning convolutional neural network (CNN) image classification algorithm. Provided a relatively heterogeneous dataset, the use of deep learning enables the development of a concrete cracks detection system that can account for several conditions, e.g. different light, surface finish and humidity that a concrete surface might exhibit. These conditions are a limiting factor when working with computer vision systems based on conventional digital image processing methods. For this work, a dataset with 3500 images of concrete surfaces balanced between images with and without cracks was used. This dataset was divided into training and testing data at an 80/20 ratio. Since our dataset is rather small to enable a robust training of a complete deep learning model, a transfer-learning methodology was applied; in particular, the open-source model VGG16 was used as the basis for the development of the model. The influence of the model’s parameters such as learning rate, number of nodes in the last fully connected layer and training dataset size were investigated. In each experiment, the model’s accuracy was recorded to identify the best result. For the dataset used in this work, the best experiment yielded a model with an accuracy of 92.2%, showcasing the potential of using deep learning for concrete crack detection.