基于单级特征金字塔的图像旋转目标检测

doi:10.3778/j.issn.1002-8331.2301-0017

摘要/Abstract

摘要： 遥感图像的目标检测任务是遥感应用领域的一个研究热点，一直受到广泛的关注。随着遥感图像分辨率的提高，遥感图像中目标的多方向性、目标大纵横比等特点更加明显，这对于已有的方法是一个挑战。针对以上问题，提出了基于单级特征金字塔的图像旋转目标检测模型。设计了单级特征金字塔结构，并结合膨胀卷积组获得目标的多尺度特征；使用分类方法处理旋转框的角度信息，结合DETR的集合预测思想，构造新的边界框回归损失，实现无锚框旋转目标检测；为了减少模型计算量并加快收敛速度，在解码器的交叉注意力上加入权重约束，将全局注意力计算限制在局部范围内。在DOTA数据集上的实验证明，该方法在提升模型检测性能的同时，有效地解决了DETR模型收敛速度慢的问题。

关键词: 遥感图像, 旋转目标检测, 单级特征金字塔, 周期局部损失, 角度分类

Abstract: The object detection task of remote sensing image is a research hotspot in the field of remote sensing applications and has been widely concerned. However, with the improvement of the resolution of remote sensing images, the arbitrary orientation and the large aspect ratio of targets in remote sensing images are more obvious, which is a challenge for existing methods. In response to the above problems, this paper proposes a image rotation object detection model based on simple feature pyramid. Firstly, a simple feature pyramid structure is designed, and the multi-scale features of the target are obtained by combining the expansion convolution group. Secondly, the classification method is used to process the angle information of the rotating box, combined with the set prediction idea of DETR, a new bounding box regression loss is constructed to realize rotating targets detection without anchor frames. Finally, in order to reduce the amount of model computation and accelerates the convergence, weight constraints are added to the cross-attention of the decoder, which limit the global attention calculation to the local range. Experiments on the DOTA dataset show that this method not only improves the model detect performance, but also effectively solves the problem of slow convergence of DETR model.

Key words: remote sensing image, rotating target detection, single feature pyramid, cyclical focal loss, angle classification

张正, 白佳华, 田青. 基于单级特征金字塔的图像旋转目标检测[J]. 计算机工程与应用, 2023, 59(15): 235-242.

ZHANG Zheng, BAI Jiahua, TIAN Qing. Image Rotating Objects Detection Based on Single Level Feature Pyramid[J]. Computer Engineering and Applications, 2023, 59(15): 235-242.

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