Object detection is a widely explored task in the field of computer vision, whose main objective is to both correctly locate and categorize all the objects within an image, separating them from the background. However, traditional object detection approaches work under closed-set condition, which means that the model can only detect objects seen during the training phase. This represents a clear limitation as it is not suitable for practical use cases, where unknown objects will unavoidably appear at test-time and the model is com- pelled to predict them as one of the known classes. Therefore a more realistic scenario is represented by open-set in object de- tection, where images containing unknown items can be submitted during testing, requiring the model to detect them too.