  Object detection is essential to many perception algorithms used in modern
robotics applications. Unfortunately, the existing models share a tendency to
assign high confidence scores for out-of-distribution (OOD) samples. Although
OOD detection has been extensively studied in recent years by the computer
vision (CV) community, most proposed solutions apply only to the image
recognition task. Real-world applications such as perception in autonomous
vehicles struggle with far more complex challenges than classification. In our
work, we focus on the prevalent field of object detection, introducing Neuron
Activation PaTteRns for out-of-distribution samples detection in Object
detectioN (NAPTRON). Performed experiments show that our approach outperforms
state-of-the-art methods, without the need to affect in-distribution (ID)
performance. By evaluating the methods in two distinct OOD scenarios and three
types of object detectors we have created the largest open-source benchmark for
OOD object detection.
