Convolutional Neural Networks have shown great results for object detection tasks by learning texture and pattern extraction filters. However, object-level interactions are harder to grasp without increasing the complexity of the architectures. On the other hand Point Process models propose to solve the detection on the configuration of objects as a whole, allowing to factor-in the image data, and the objects' interaction priors. In this paper we propose combining the information extracted by a CNN with priors on objects within a Markov Marked Point Process framework. We also demonstrate a method to learn the parameters of this Energy Based Model. We apply this model to the detection of small vehicles in optical satellite imagery, where the image information needs to be complemented with object interaction priors because of noise and small object sizes. The code will be made available at github.com/Ayana-Inria.