Abstract: Open-world object detection is a challenging visual task that bridges the gap between traditional object detection and that in real-world scenarios.Unlike traditional methods confined to a limited set of classes,open-world object detection requires not only the identification and detection of objects from known (seen) classes but also the ability to label and gradually learn objects from unknown (unseen) classes.When traditional object detection techniques are directly applied to open-world scenarios,two major problems often arise:first,they might treat unknown classes as background and ignore them; second,they might misclassify unknown classes as known ones.To tackle these problems,this study proposes the utilization of annealing algorithms to separate features of known and unknown classes,guiding the learning process of the detection model.The introduction of the annealing module leads to an improvement in the accuracy of unknown classes,but a slight decrease in the accuracy of known classes.To address this,an efficient channel attention module is incorporated to enhance the accuracy of known classes.Compared to previous methods,this approach demonstrates superior performance in detecting objects from both known and unknown classes.