In real-world applications where confidence is key, like autonomous driving, the accurate detection and appropriate handling of classes differing from those used during training are crucial. Despite the proposal of various unknown object detection approaches, we have observed widespread inconsistencies among them regarding the datasets, metrics, and scenarios used, alongside a notable absence of a clear definition for unknown objects, which hampers meaningful evaluation. To counter these issues, we introduce two benchmarks: a unified VOC-COCO evaluation, and the new OpenImagesRoad benchmark which provides clear hierarchical object definition besides new evaluation metrics. Complementing the benchmark, we exploit recent self-supervised Vision Transformers performance, to improve pseudo-labeling-based OpenSet Object Detection (OSOD), through OW-DETR++. State-of-the-art methods are extensively evaluated on the proposed benchmarks. This study provides a clear problem definition, ensures consistent evaluations, and draws new conclusions about effectiveness of OSOD strategies.

本文解决了在真实应用中自信度至关重要的开放集对象检测问题，指出了目前方法在数据集、评价指标和场景上的不一致性以及对未知对象定义的缺乏。本文提出了两个新的基准：统一的VOC-COCO评估和新的OpenImagesRoad基准，并通过OW-DETR++改进了基于伪标签的开放集对象检测，确保了一致的评估标准，并得出有关OSOD策略有效性的新的结论。

开放集对象检测：朝着统一的问题表述和基准测试