Redefining Object Detection for Open-World Settings: A Framework for Simultaneous Identification of Known and Unknown Classes

Traditional closed-world object detection methods are limited to a predefined set of classes and struggle to recognize objects beyond these boundaries. This work proposes an improved Open-World Object Detection (OWOD) methodology that supports the identification and Incremental Detection of both known and unknown objects. Existing OWOD methods typically face uncertainties due to the adoption of complex and ambiguous strategies for discovering the unknown objects. Such approaches lower the detection accuracy of the known classes. To effectively tackle these challenges, this study presents a new Open-World Novelty and Outlier Detection (OWNOD). The framework integrates a dynamic thresholding mechanism a customized loss function and statistical verification technique optimize the simultaneous detection of both known and unknown objects. By combining these approaches, OWNOD enhances the balance between identifying new, unseen objects and maintaining accuracy for previously learned classes, effectively addressing the inherent complexities of open-world object detection. We benchmark against state-of-the-art methods, that includes the baseline ORE and ABLTL. We also report significant improvements across multiple metrics.For instance, in Task 1, K-mAP improved by 3.10 points, U-mAP by 2.20 points, and U-Recall by 9.50 points. In Task 2, K-mAP improved by 5.65 points, U-mAP by 2.43 points, and U-Recall by 2.46 points. In Task 3, we achieved gains of 4.14 points for K-mAP, 0.34 points for U-mAP, and 6.62 points for U-Recall. For Task 4, where all classes are known, we report a K-mAP improvement of 5.89 points, demonstrating the robustness and effectiveness of our model.