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.

翻译：目标检测是现代机器人应用中许多感知算法的核心。然而，现有模型存在对分布外样本赋予高置信度分数的普遍倾向。尽管计算机视觉领域近年来对分布外检测进行了广泛研究，但大多数提出的解决方案仅适用于图像识别任务。诸如自动驾驶车辆感知等实际应用面临的挑战远比分类问题复杂。在本文中，我们聚焦于目标检测这一主流领域，提出了针对目标检测中分布外样本检测的神经元激活模式方法（NAPTRON）。实验结果表明，我们的方法在不影响分布内性能的前提下，超越了现有最优方法。通过在两种不同的分布外场景和三种类型的目标检测器上评估，我们构建了目前最大的分布外目标检测开源基准。