Artificial Intelligence is evolving at an accelerating pace alongside the increasing number of large datasets due to vast number of image data on the Internet. Unnamed Aircraft Vehicles (UAVs) are also a new trend that will have a huge impact over the next years. The use of UAVs arises some safety issues, such as collisions with dynamic obstacles like birds, other planes, or random thrown objects. Those are complex and sometimes impossible to avoid with state-of-the-art algorithms, representing a threat to the applications. In this article, a new video dataset of collisions, entitled ColANet, aims to provide a base for training new Machine Learning algorithms for handling the problem of avoiding collisions with high efficiency and robustness. It is also shown that using this dataset is easy to build new neural network models and test them.