关键词: 开放世界目标检测, 反向传播梯度, 图分割算法, 特征范数, 卷积神经网络

Abstract: Open-World Object Detection (OWOD) extends the object detection task to real and variable environments, requiring models to accurately identify known and unknown objects, and gradually learn new knowledge. In response to the low recall rate of unknown classes and the problem of false identification in existing open-world object detection methods, Gradient-discriminative and feature norm-driven open-world object detection method (GDFN-OWOD) is proposed. To address the issue of low recall rate for unknown classes, a Gradient-Discriminative Representation Module (GDRM) is proposed, which uses the gradient difference from backpropagation to accurately distinguish unknown classes from the background, thereby improving the recall rate of unknown classes. In addition, a Graph-Based Segmentation for Bounding Box Clustering (GSBC) algorithm is introduced, modeling the determination of object bounding boxes as a graph decomposition problem, reducing redundant bounding boxes, and thus reducing the computational load of the model. To tackle the problem of false identification of unknown classes, a FeatureNorm-Based Classifier (FN-BC) is employed, selecting the most optimal convolutional layer to recognize known and unknown classes for higher recognition accuracy. Experimental results on the OWOD dataset show that the recall rate of unknown classes in tasks T1, T2, and T3 has been increased by 1.1%, 2.1%, and 0.9%, respectively, and the absolute open-set error (A-OSE) has been reduced by 35.1%, 28.7%, and 12.2%, respectively. Compared with existing open-world object detection methods, it effectively alleviates the problems of low recall rate for unknown classes and false identification.

Key words: open-world object detection, backpropagated gradient, Graph segmentation algorithm, feature norm, convolutional neural network