In this project we can see two fields that are very popular nowadays, Big Data and Deep Learning. In particular, the object detection is in the field of artificial vision. But we are not looking for detect an object within the image, we try to solve a problem. The problem to resolve is when we don't have a dataset large enough to get an optimal result. These days this problem persist, if we develop an application to looking for a rare animal species that is endangered, is more probably that the images that we have from this animal will be not enough to get a good result for detect them. To resolve this problem we have used two different technologies. The first one, it has proved it efficiency detecting faces, this is the Haar-Cascade over OpenCV. The second one is relatively newest, it is called transfer learning. In the case of transfer learning we use a framework called Cognitive Toolkit developed by Microsoft and also we use Azure cloud service developed by Microsoft. To prove the efficiency of this two methods we have tried to detect different objects: Watches, wolfs, sheep and helipad signs. We have chosen this last object because there isn't any dataset of it and this makes it closer to a one real case. Finally, we will test in practical way. If all we planned before is possible we will be able to find out interesting ideas for the future.