This paper addresses the significant challenge in open-set object detection (OSOD): the tendency of state-of-the-art detectors to erroneously classify unknown objects as known categories with high confidence. We present a novel approach that effectively identifies unknown objects by distinguishing between high and low-density regions in latent space. Our method builds upon the Open-Det (OD) framework, introducing two new elements to the loss function. These elements enhance the known embedding space's clustering and expand the unknown space's low-density regions. The first addition is the Class Wasserstein Anchor (CWA), a new function that refines the classification boundaries. The second is a spectral normalisation step, improving the robustness of the model. Together, these augmentations to the existing Contrastive Feature Learner (CFL) and Unknown Probability Learner (UPL) loss functions significantly improve OSOD performance. Our proposed OpenDet-CWA (OD-CWA) method demonstrates: a) a reduction in open-set errors by approximately 17%-22%, b) an enhancement in novelty detection capability by 1.5%-16%, and c) a decrease in the wilderness index by 2%-20% across various open-set scenarios. These results represent a substantial advancement in the field, showcasing the potential of our approach in managing the complexities of open-set object detection.

翻译：本文解决了开放集目标检测（OSOD）中的重大挑战：现有最优检测器倾向于以高置信度错误地将未知物体分类为已知类别。我们提出了一种新颖方法，通过区分潜在空间中的高密度与低密度区域来有效识别未知物体。该方法基于Open-Det（OD）框架，在损失函数中引入两个新元素，增强了已知嵌入空间的聚类性并扩展了未知空间的低密度区域。第一个新增项是类Wasserstein锚点（CWA），一种优化分类边界的新函数；第二个是谱归一化步骤，提升了模型的鲁棒性。这些增强与现有对比特征学习器（CFL）和未知概率学习器（UPL）损失函数相结合，显著提升了OSOD性能。我们提出的OpenDet-CWA（OD-CWA）方法在多种开放集场景下实现：a) 开放集错误减少约17%-22%，b) 新颖性检测能力提升1.5%-16%，c) 荒野指数降低2%-20%。这些结果代表了该领域的重大进展，展示出我们的方法在处理开放集目标检测复杂性方面的潜力。