采用自适应预筛选的遥感图像目标开集检测研究

In open, dynamic environments where the range of object categories continually expands, the challenge of remote sensing object detection is to detect a known set of object categories while simultaneously identifying unknown objects. To this end, a remote sensing open-set object detection network based on adaptive pre-screening is proposed. Firstly, an adaptive pre-screening module is proposed for object region proposals. Based on the coordinates of the selected region proposals, queries with rich semantic information and spatial features are generated and passed to the decoder. Subsequently, a pseudo-label selection method is devised based on object edge information, and loss functions are constructed with the aim of open set classification to enhance the network’s ability to learn knowledge of unknown classes. Finally, the Military Aircraft Recognition (MAR20) dataset is used to simulate various dynamic environments. Extensive comparative experiments and ablation experiments show that the proposed method can achieve reliable detection of known and unknown objects.