Open-world object detection (OWOD) is a challenging task requiring models to detect both known and unknown objects while incrementally learning from new data. Current OWOD methods typically label regions with high objectness scores as unknown objects, relying heavily on known object supervision, leading to label bias. To address this, we propose object reconstruction error modeling, using object-level semantic information for unsupervised foreground and background modeling. Additionally, we introduce an unsupervised proposal generation method, leveraging segment anything model’s zero-shot learning to generate pseudo-labels for unknown objects. However, classifiers trained on known categories tend to bias toward them during inference. To resolve this, we propose a location-enhanced network, reframing classification as a location quality prediction task. Our method achieves a significant 37% improvement in unknown category recall (52.1%) on the Microsoft common objects in context (MS-COCO) dataset, outperforming previous state-of-the-art methods while maintaining competitive performance on known objects. Furthermore, it surpasses deformable detection transformer (DETR)-based models, achieving 10.95 frames per second, with a speed advantage over faster region-based convolutional neural network (Faster R-CNN)-based methods.

Open World Object Detection (OWOD) extends the problem of object detection to more complex realworld dynamic scenarios, requiring the system to recognize all known and unknown object categories in the image and possess the capability for incremental learning based on newly introduced knowledge. However, current OWOD methods typically mark regions with high object scores as unknown objects and largely rely on supervision of known objects. Although these methods can detect unknown objects that are similar to known ones, they suffer from a significant label bias problem, where regions dissimilar to known objects are often misclassified as part of the background. To address this issue, this study first proposed an unsupervised region proposal generation method based on a large visual model to enhance the model’s ability to detect unknown objects. Then, considering that the sensitivity of the Region of Interest (ROI) classification stage to new categories during model training can affect the generalization performance of the Region Proposal Network (RPN) in the proposal generation stage, a decoupled joint training method for RPN region proposal generation and ROI classification was introduced to improve the model's capability to resolve label bias problems. Experimental results show that the method proposed in this study has achieved a significant improvement in detecting unknown objects on the MS-COCO dataset, with the unknown category recall rate exceeding that of the previous SOTA methods by more than twice, reaching 52.1%, while maintaining competitiveness in detecting known object categories. In terms of inference speed, the model, constructed using pure convolutional neural networks rather than dense attention mechanisms, achieves a frame rate 8.18 f/s higher than that of deformable DETR-based methods.