Robustness is a fundamental aspect for developing safe and trustworthy models, particularly when they are deployed in the open world. In this work we analyze the inherent capability of one-stage object detectors to robustly operate in the presence of out-of-distribution (OoD) data. Specifically, we propose a novel detection algorithm for detecting unknown objects in image data, which leverages the features extracted by the model from each sample. Differently from other recent approaches in the literature, our proposal does not require retraining the object detector, thereby allowing for the use of pretrained models. Our proposed OoD detector exploits the application of supervised dimensionality reduction techniques to mitigate the effects of the curse of dimensionality on the features extracted by the model. Furthermore, it utilizes high-resolution feature maps to identify potential unknown objects in an unsupervised fashion. Our experiments analyze the Pareto trade-off between the performance detecting known and unknown objects resulting from different algorithmic configurations and inference confidence thresholds. We also compare the performance of our proposed algorithm to that of logits-based post-hoc OoD methods, as well as possible fusion strategies. Finally, we discuss on the competitiveness of all tested methods against state-of-the-art OoD approaches for object detection models over the recently published Unknown Object Detection benchmark. The obtained results verify that the performance of avant-garde post-hoc OoD detectors can be further improved when combined with our proposed algorithm.

翻译：鲁棒性是开发安全可信模型的基本要素，尤其在开放世界部署时更为关键。本研究分析了单阶段目标检测器在分布外数据存在情况下的固有鲁棒运行能力。具体而言，我们提出了一种新颖的未知目标检测算法，该算法利用模型从每个样本中提取的特征。与文献中其他近期方法不同，我们的方案无需重新训练目标检测器，从而可直接使用预训练模型。所提出的分布外检测器通过应用监督降维技术来缓解模型特征维度灾难的影响，并利用高分辨率特征图以无监督方式识别潜在未知目标。实验分析了不同算法配置和推理置信度阈值下已知目标与未知目标检测性能的帕累托权衡。我们将所提算法与基于逻辑值的后验分布外检测方法及可能的融合策略进行性能比较。最后，我们基于最新发布的未知目标检测基准，讨论了所有测试方法与最先进分布外检测方法的竞争力。实验结果表明，当现有前沿后验分布外检测器与我们提出的算法结合时，其性能可获得进一步提升。