采用自适应预筛选的遥感图像目标开集检测研究

Research on Open-Set Object Detection in Remote Sensing Images Based on Adaptive Pre-Screening

开放动态环境下目标类别不断丰富,遥感目标检测问题不能局限于已知类目标的鉴别,还需要对未知类目标做出有效判决。该文设计一种基于自适应预筛选的遥感开集目标检测网络,首先,提出面向目标候选框的自适应预筛选模块,依据筛选出的候选框坐标得到具有丰富语义信息和空间特征的查询传递至解码器。然后,结合原始图像中目标边缘信息提出一种伪标签选取方法,并以开集判决为目的构造损失函数,提高网络对未知新类特征的学习能力。最后,采用MAR20飞机目标识别数据集模拟不同的开放动态遥感目标检测环境,通过广泛的对比实验和消融实验,验证了该文方法能够实现对已知类目标的可靠检测和未知类目标的有效检出。

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.